Road surveillance video dataset. This dataset can be used for two tasks.

In the literature, various methods have been investigated for accident detection, among which deep learning approaches have shown superior accuracy compared to other methods. CADP dataset provides samples for accident detection and forecasting type analysis ; Average length of videos in our dataset is 366 frames per video with longest video consisting of 554 frames ; Time to accident - duration from time 0 in video to onset of first accident in annotated videos is 3. GTS specializes in Video Data Collection, where we systematically capture and categorize video content. Dec 30, 2019 · Intelligent vehicle detection and counting are becoming increasingly important in the field of highway management. The authors used convolutions and bi-directional recurrent layers for the same. This paper proposes two different Convolutional Neural One of the most famouse large-scale dataset video anomaly detection dataset with video-level labels is UCF-crime dataset that contains 1,900 untrimmed real-world outdoor and indoor surveillance videos. Dec 24, 2023 · Video anomaly detection algorithms are yet to advance at the pace CCTV footage data of public places is being recorded and made publicly available. The model is then fine-tuned by the supplemental dataset to validate the effectiveness of parameter-based transfer learning. The researchers developed various computational intelligence algorithms for image defogging to improve the visibility of drivers. May 25, 2022 · We present TIMo (Time-of-flight Indoor Monitoring), a dataset for video-based monitoring of indoor spaces captured using a time-of-flight (ToF) camera. Aug 31, 2022 · We present a new road accident video dataset (MP-RAD), where each accident event is synthetically generated and captured from five independent camera perspectives using a computer gaming platform Oct 1, 2022 · For various real-time datasets of video surveillance systems, the experimental results indicate accuracy, precision, recall, and F-1 score. These data included three kinds of vehicles (large, medium, and small). The surveillance camera video file was divided into video fragments consisting of 28 video frames. 098 Corpus ID: 42931833; Accurate seat belt detection in road surveillance images based on CNN and SVM @article{Chen2018AccurateSB, title={Accurate seat belt detection in road surveillance images based on CNN and SVM}, author={Yanxiang Chen and Gang Tao and Hongmei Ren and Xinyu Lin and Luming Zhang}, journal={Neurocomputing}, year={2018}, volume={274}, pages={80 Mini-drone [6] UAV Videos 3 3 7 7 3 - > 27K Surveillance < 10 Mars [32] CCTV Video 7 7 3 7 7 1,261 20K Surveillance - AVI [23] UAV Still 7 7 7 7 3 5,124 10K Surveillance [2;8] DukeMTMC-VideoReID [27] CCTV Video 7 7 3 7 7 1,812 815K Surveillance - iQIYI-VID [18] (Various) Video 7 7 7 3 7 5,000 600K TV - DRone HIT [10] UAV Still 7 7 3 7 7 101 40K Explore and run machine learning code with Kaggle Notebooks | Using data from Road Traffic Video Monitoring Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. The syntheti-cally trained model is then tested on real-world road traffic surveillance videos. The dataset consists of 3100 frames from each viewpoint, containing 18883 individual annotations on the pole viewpoint, and 50274 individual annotations on the drone viewpoint. In the last decade, the num-ber of open road datasets [1,2,4,6,7,10,13,15,16,22–25] for 2D and 3D road object detection, single and multi-ple object tracking, object segmentation tasks have signif-icantly increased. With billions of surveillance video captured all over the world, multiple-object tracking and behavior analysis by manual labor are cumbersome and cost expensive. as well as normal activities. The current deep-learning based approaches suffer from the lack of large-scale traffic accident video datasets due to the privacy concerns. For experiments, the synthetic dataset is divided into 60% train-ing, 20% validation, and 20% test sets. It comes with precomputed audio-visual features from billions of frames and audio segments, designed to fit on a single hard disk. May 24, 2023 · Anomaly detection in video surveillance is a highly developed subject that is attracting increased attention from the research community. Due to this and the wide use of video surveillance and intelligent traffic systems, an automated traffic accident detection approach becomes Oct 16, 2020 · This paper describes a method for learning anomaly behavior in the video by finding an attention region from spatiotemporal information, in contrast to the full-frame learning. The dataset includes videos collected from both stationary ground cameras and moving aerial vehicles. Data On top of such information, we calculate vehicle lane positions and two types of road congestion metrics. We built two kinds of dataset, a dynamic database and a static database. Due to the sparse occurrence of anomalous events, anomalous activity recognition from surveillance videos is a challenging research task. There is a need for a lightweight, robust and 24X7 video-monitoring system that automates this process. Therefore, real-time, automatic and accurate detection of abnormal events has become the main goal of video-based surveillance systems. This process entails recording high-quality footage, analyzing it frame-by-frame, and labeling objects for machine recognition. A detailed review on anomaly detection in road traffic accidents is extensively covered in [3] giving pointers to address issues in Intelligent Transportation Systems. The main intention of this paper is to analyse the various types of foggy Feb 10, 2023 · Video surveillance systems are often used for traffic monitoring and to characterize traffic load. Furthermore we benchmark SOTA models for four multimodal tasks on this newly created dataset which serve as new baselines for surveillance VALU. They can also be re-organized and used for semi-supervised settings that the training data contains normal videos only. Nov 25, 2020 · Intelligent video surveillance (IVS) technology is widely used in various security systems. Car Crash Dataset (CCD) is collected for traffic accident analysis. We find that bicycles, forward-looking tricycles and motorcycles are similar in their riding state. g. By and large, the task of accident detection for traffic video surveillance has been for- Surveillance Perspective Human Action Recognition Dataset: 7759 Videos from 14 Action Classes, aggregated from multiple sources, all cropped spatio-temporally and filmed from a surveillance-camera like position. 3. Through experiments, we find that mainstream models used in previously public datasets perform poorly onsurveillance video, demonstrating new challenges in surveillance VALU. The total length of the videos is 128 hours, which contains 13 classes of anomalous events including: 1. Thisdatasetisintendedfora very different formulation of video anomaly detection more akin to activity detection. Differently, Sultani et al [38] constructed a real This dataset is benefited for developing a fight detection system which is aimed to use in surveillance cameras in public areas such as streets, underground stations and more. This not only enhances pollution but also leads to several road accident fatalities which may be greatly reduced by proper monitoring and surveillance. In addition, we propose a new feature fusion method, which improves the prediction performance of the model, and obtains higher accuracy, F1 parameters, etc. npy and name_labels. Sep 1, 2022 · Experimentation on IITH road accident dataset4. The UCF–Crime dataset is currently the most realistic Jun 1, 2023 · The UCFCrime dataset includes lengthy surveillance video camera feeds covering 13 different classes of anomaly events such as abuse, arrest, arson, assault, road accidents, burglaries, explosions, fighting, robbery, shooting, stealing, shoplifting, and vandalism in addition to the normal events class. wav as well as . 1%, respectively, which is a Apr 26, 2019 · Due to the rapid development of deep learning algorithms in recent years, automatic object tracking and behavior analysis put forward an urgent demand on a large scale well-annotated surveillance video dataset that can reflect the diverse, congested, and complicated scenarios in real applications. We expect the dataset to further research in Nov 23, 2022 · We present a new road accident video dataset (MP-RAD), where each accident event is synthetically generated and captured from five independent camera perspectives using a computer gaming platform. We collect 3888 hours of video (over 5 TeraBytes of data in total and 72 hours of video for each camera captured from 7:00 to 19:00 for 6 days) Aug 19, 2022 · ETH Pedestrian Dataset. Vision-based detection of road accidents using traffic surveillance video is a highly desirable but challenging task. Person detection for people counting and anomaly detection are the two targeted applications. Through experiments we find that mainstream models used in previously public datasets perform poorly on surveillance video demonstrating new challenges in surveillance VALU. Diverse, Pre-processed, Augmented and Clean Data ready for training Dataset Information. 3: Video length and number of skeleton trajectories distribution in HR-Crime. . This paper introduces an urban surveillance Furthermore, we benchmark SOTA models for four multimodal tasks on this newly created dataset, which serve as new baselines for surveillance VALU. The UCF-Crimes [20] also has a category for road incidents with long videos, but only Jan 24, 2018 · Dataset. ). Jan 3, 2024 · Car accident detection plays a crucial role in video-based traffic surveillance systems, contributing to prompt response and improved road safety. 265/HEVC, H. 2016. All the vehicles in the GRAM-RTM dataset have been manually annotated. Nov 9, 2021 · According to worldwide statistics, traffic accidents are the cause of a high percentage of violent deaths. The time taken to send the medical response to the accident site is largely affected by the human factor and correlates with survival probability. ETH is a dataset for pedestrian detection. The UCF-Crime dataset is a large-scale dataset of 128 hours of videos. Video length: The average length of the videos in the CADP dataset is 366 frames per video, which is 3. It includes a traffic video sequence of 90 minutes long. In the traffic scene, there are cars, motorcycles, bicycles, tricycles and other types of vehicles. Staging fake accidents with real cars is expensive, and car crashes are rare incidents in roadside CCTV footage. World Health Organization, 2018. The second file represents an annotated matrix. These anomalies are selected because they have a significant impact on public 2. The resulted HR-Crime dataset consists of 789 human-related anomaly videos and 782 human-related normal videos. In computer vision tasks, utilizing action recognition (AR) has contributed to high-precision video surveillance, medical imaging, and digital We proposed a large-scale road abandoned object dataset from surveillance perspective. 8% and 98%, 96. Each frame is represented by a pair of files: named. Most of the existing road accident datasets use egocentric views or they are captured in fixed camera setups. The effectiveness of the proposed model is tested on real-world traffic surveillance video, namely, IITH road accident dataset [23]. C. Fights in parking lots, bars, restaurants and public places can be avoided if there is a system that does real time detection. The first file represents a matrix of pixel values of the detection line. Further, with the advent of UAV technology and due to the incompatibility of traditional techniques, surveillance has become one Mar 31, 2022 · Using an AVDL dataset with annotated video surveillance data, deep learning network models can be trained to detect traffic flows, track pedestrians, and determine vehicle counts. Nov 27, 2023 · Accident detection and public traffic safety is a crucial aspect of safe and better community. An anomaly specifies unusual activity or response in a video by one or more subjects/objects present in the video clip. Mar 2, 2024 · This paper introduces an urban surveillance video dataset (USVD) which is by far the largest and most comprehensive. camera motion and illumination, are usually ideal thus realistic conditions are not well reflected; 3) events are accurately detected as well as localized anomalies on surveillance video with more expressive features as well as a more complicated design. 36th International Conference on Machine Learning, ICML 2019. Nov 26, 2021 · Furthermore, in contrast with previous works, which mainly focused on hand-crafted datasets, our dataset took real-time surveillance camera feeds with different subjects and environments. The size of the scene is 720 by 480 and it is divided into 20 clips. Dec 27, 2021 · A new traffic image dataset was created to train the models, which includes real traffic images in poor lightning or weather conditions and low-resolution images. As such, we look at the this paper utilizes synthetic road surveillance data to train a YOLOv5 model for detecting road events from surveillance videos. Aug 26, 2023 · With the popularity of video surveillance technology, people are paying more and more attention to how to detect abnormal states or events in videos in time. camera motion and illumination, are usually ideal thus realistic conditions are not well reflected; 3) events are The third video sequence, called Urban1 (23435 frames), has been recorded in a busy intersection with video surveillance traffic camera with a resolution of 600x360 @25fps. YouTube-8M is a large-scale labeled video dataset that consists of millions of YouTube video IDs, with high-quality machine-generated annotations from a diverse vocabulary of 3,800+ visual entities. The approaches reported can be Jan 31, 2023 · Anomaly detection has significant importance for developing autonomous surveillance systems. In addition, we classify normal and abnormal events and show the method’s ability to find the right category for each anomaly. The dataset consists of 16 scenes captured in 7 typical outdoor scenarios: street, crossroads, hospital entrance, school gate, park, pedestrian mall, and public square. CCTV Dec 26, 2022 · The proposed technique was validated on a large-scale dataset comprising roughly 2000 full day sequences (roughly 400K video frames, of which 300K unlabelled), acquired from several road-side Sep 20, 2020 · Furthermore, in [42], an aerial dataset of a road scene is produced using two different points of view: a static camera and a drone; each video consist of 3100 frames, and the two acquisitions are Jan 24, 2018 · Dataset. The dataset is captured from a stereo rig mounted on a car, with a resolution of 640 x 480 (layered), and a framerate of 13–14 FPS. datasets such as Kinetic [21], GRAM Road-Traffic Monitoring [22] or MIT Traffic dataset [23] We introduce a new large-scale video dataset designed to assess the performance of diverse visual event recognition algorithms with a focus on continuous visual event recognition (CVER) in outdoor areas with wide coverage. There is great demand for intelligent systems with the capacity to automatically detect anomalous events in streaming videos. npy. Jul 21, 2023 · The use of machine learning and computer vision techniques for detecting road accidents is a challenging task due to the limited availability of accident data for training. Video data and the tools for automated analysis have a great potential to be used in road traffic research, particularly road safety. This paper proposes a comprehensive and HR-Crime: Human-Related Anomaly Detection in Surveillance Videos 5 (a) Video length in minutes (b) Skeleton trajectories Fig. Road side video surveillance in traffic scenes using map-reduce framework for accident analysis. Data Collection The videos in the dataset are captured from 54 surveillance cameras distributed in public places. Thus, the TACS dataset contains a wide variety of data and provides researchers with rich annotations for traffic-related algorithms. , Rajalakshmi, M. We collect 3888 hours of video (over 5 TeraBytes of data in total and 72 hours of video for each camera captured from 7:00 to 19:00 for 6 days) Instance-level annotations of road users in RGB and thermal video AAU RainSnow Traffic Surveillance Dataset | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. In contrast to other public datasets, this one contains many more videos with distortions and diversified content from common video surveillance scenarios. The details of the implementation and experiments are included in our paper "Vision-based Fight Detection From Surveillance Cameras" which is presented at IPTA 2019 Mar 31, 2022 · A new Annotated Virtual Detection Line (AVDL) dataset is presented for multiple object detection, consisting of 74,108 data files and 74,108 manually annotated files divided into six classes: Vehicles, Trucks, Pedestrians, Bicycles, Motorcycles, and Scooters from the video. 69 seconds May 17, 2023 · The proposed model has been trained using the open-source dataset Crash Car Detection Dataset, and its produced precision, recall, and mAP are 93. The following table provides a statistical comparison between the UCA dataset and other traditional video datasets in multimodal learning tasks. This approach used action recognition, object recognition, and human-specific datasets like UET Video Surveillance, AGRIINTRUSION, Jun 9, 2021 · Surveillance cameras are being installed in many primary daily living places to maintain public safety. Feb 24, 2022 · The CRCV Real-world Anomaly Detection Dataset consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies such as fighting, road accident, burglary, robbery, etc. In our proposed method, a robust background subtraction (BG) for extracting motion, indicating the location of attention regions is employed. In this paper, we introduce VIRAT Video Dataset which is a new large-scale surveillance video dataset designed to assess the performance of event recognition algorithms in realistic scenes1. The data were captured from real road scenes using 50 video cameras Urban traffic accident detection in surveillance videos is an essential task in intelligent traffic monitoring. Within this May 31, 2024 · Road abandoned objects are potential safety hazards in modern traffic transport, especially in highway scenes. Oct 17, 2023 · Vehicle trajectory data underpins various applications in intelligent transportation systems, such as traffic surveillance, traffic prediction, and traffic control. ROAD is designed to test an autonomous vehicle's ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations. A new high definition highway vehicle dataset Another dataset from Sultani et al. 264/AVC, and VP9), 3 target bitrates (1,000 kbps, 2,000 kbps, and 4,000 kbps) Aug 16, 2022 · The KITTI benchmark dataset contains images of highway scenes and ordinary road scenes used for automatic vehicle driving and can solve problems such as 3D object detection and tracking. Manual monitoring of social distancing norms is impractical with a large population moving about and with insufficient task force and resources to administer them. Compared with the common Secondary Radar or Automatic Dependent Surveillance-Broadcast (ADS-B) , video data contains more information, so video based intelligent surveillance is more promising in the next generation of airport management system. Vishnu, M. To bridge this gap, we propose UCA, as the first multimodal surveillance video dataset, which is derived from re-annotating UCF-Crime. There Sep 26, 2022 · An open-sourced datasets targeting on freeway traffic accidents collected from surveillance cameras is in great need and of practical importance. ROAD comprises videos originally from the Oxford RobotCar Dataset, annotated with bounding boxes showing the location in the image plane of each road event. This dataset can be used for two tasks. The video samples in the created dataset were collected from various publicly accessible websites and downloaded free Dataset for Highway Traffic Analysis through CCTV captured footage. We collected a dataset of real accident videos from the CCTV surveillance network of Hyderabad City in India. No onboard data Jul 18, 2021 · Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. MIT Traffic is a dataset for research on activity analysis and crowded scenes. It consists of 10,000 images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads. neucom. posed a method for understanding the video streams and storing them as text (to save on storage space) using natural language generation. " A critical task in video surveillance is detecting anomalous events such as traffic accidents, crimes or illegal Jan 24, 2018 · DOI: 10. Jan 12, 2018 · We also introduce a new large-scale first of its kind dataset of 128 hours of videos. This paper presents a novel Sep 25, 2023 · Surveillance videos are an essential component of daily life with various critical applications, particularly in public security. In order to help the vision community address these shortcomings, we endeavor to collect video data of real traffic accidents that covered abundant scenes. Most existing Jun 16, 2021 · In the contemporary era, the global explosion of traffic has created many eye-catching concerns for policymakers. In this paper, we propose a framework to synthesize traffic videos containing both normal traffic and accident events by simulating the real The abundant presence of surveillance cameras result in huge volumes of video data, which need to be monitored constantly. The time period required for an anomalous activity to be completely Jun 22, 2024 · However, recent audio and speech, as well as video datasets 13,14,29,37,38,39,40 derived from YouTube are generally deployed with . The limitation for proposed video surveillance is in particular, utilising the intrinsic location of anomalies and examining if use of spatiotemporal data might aid in the detection of abnormalities. Due to this, a wide variety of approaches have been proposed to build an effective model that would ensure public security. avi format, and various researchers Comparative Analysis with Other Video Datasets. CVQAD: Video Compression Dataset and Benchmark of Learning-Based Video-Quality Metrics (NeurIPS 2022 Track Datasets and Benchmarks) [ Paper ][ Homepage ] 1,022 compressed videos, 32 encoders of 5 compression standards (H. Despite technical developments in modern science, abnormal event detection in 19-min video), NGSIM dataset for road traffic modeling, and help the road surveillance. It contains real traffic accident videos captured by dashcam mounted on driving vehicles, which is critical to developing safety-guaranteed self-driving systems. mainly for improving security and public safety [1-4]. Previous datasets for action recognition are unrealistic for real-world surveillance because they consist of short clips showing one action by one individual [15, 8 Aug 31, 2022 · The USD Ped2 dataset contains 28 surveillance video clips that were also targeted to be used for studies regarding anomaly detection on busy streets. Deep learning is a growing technology that extracts each object’s feature without human intervention. We have also created ground truth summaries for the UCF-Crime video dataset. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies including Abuse, Arrest, Arson, Assault, Road Accident, Burglary, Explosion, Fighting, Robbery, Shooting, Stealing, Shoplifting, and Vandalism. The following categories are provided: car, truck, van, and big-truck. Surveillance videos have a major contribution in unstructured big data. Among the widespread examples of big data, the role of video streams from CCTV cameras is equally important as other sources like social media data, sensor data, agriculture data, medical data and data evolved from space research. The dataset divides traffic congestion into three categories (congestion, low speed, and normal speed) and considers rainy and sunny conditions. The popularity of deep learning stems from its ability to automatically The dataset is collected from the Youtube videos that contains fight instances in it. Since the airport ground is very broad, it needs a network of multiple cameras to cover the whole In the current times, the fear and danger of COVID-19 virus still stands large. 1 Available dataset for road traffic accident detection. The longest video has 554 frames. As shown in Table 1, existing datasets can be categorized into three main classes by the viewpoint perspective: Bird’s Eye View (BEV), Dashcam, and Surveillance. Aug 20, 2023 · With the enforcement of privacy laws, it is challenging to get publicly available real-world road surveillance videos for training computer vision models. DCSASS Dataset | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. The detection and recognition of abnormal activity in a real-world environment is a big challenge, as there can be many types of alarming and abnormal activities, such as theft, violence, and Road accident dataset consists of 796 videos under *. It is recorded by a stationary camera. On top of ground truth labels, the dataset also features metadata such as timeofday and the weather. The frames from each video capture have been synchronized in time and all road users have been carefully annotated down to pixel-level accuracy. We also introduce a new large-scale first of its kind dataset of 128 hours of videos. Monitoring traffic flow in smart cities using different surveillance cameras plays a crucial role in recognizing accidents and alerting first responders. [It] can be considered as coarse level video understanding, which filters out anomalies from normal patterns. In this work, a neoteric framework for detection of road accidents is proposed. We expect the dataset to further research in contrast to the easily accessible road surveillance data, industrial video data is not easy to capture because (1) video information cap-tured within factories would raise concerns regarding privacy and security, making it exceedingly difficult to achieve public access to such data for dataset creation; (2) From an anomaly setting per- Dec 19, 2022 · (1) Background: The research area of video surveillance anomaly detection aims to automatically detect the moment when a video surveillance camera captures something that does not fit the normal pattern. Car Accidents Dataset Statistics of our dataset can be found in Table 1 and Fig-ure 2. mp4 or . on the detection line in the video image. We propose a novel dataset for road scene understanding in unstructured environments where the above assumptions are largely not satisfied. * There are 300 videos in total as 150 fight + 150 non-fight * Videos are 2-second long * Only the fight related parts are included in the samples Weakly supervised video anomaly detection assumes the availability of the video-level labels and aims at detecting frame-level anomalies. Autoencoder being a powerful unsupervised method, has been popularly used for anomaly detection in various domains including Sep 19, 2020 · The frames from each video capture have been synchronized in time and all road users have been carefully annotated down to pixel-level accuracy. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies such as fighting, road accident, burglary, robbery, etc. Oct 31, 2023 · Our Car Accident Detection and Prediction (CADP) dataset consists of 1,416 video segments collected from YouTube, with 205 video segments have full spatio-temporal annotations. Jan 20, 2022 · Real-world surveillance video anomaly detection dataset We construct a new large-scale dataset, called UCF-Crime, to evaluate our method. This has mainly four drawbacks: 1) events are controlled and predictable because they are usually performed by actors; 2) environmental conditions, e. Examples are shown in Figure 2. Dataset link: updating; C3D Extractor: Learning Spatiotemporal Features with 3D Convolutional Networks (Du Tran et al. Therefore, simulating fake car crashes using computers can be a feasible option. The UCSD dataset includes 20-min surveillance videos of a highway in Seattle, Washington, USA, with a resolution of 320*240. However, current surveillance video tasks mainly focus on classifying and localizing anomalous events. Traditional vehicle trajectory Sep 16, 2018 · A novel dataset for traffic accidents analysis is presented to resolve the lack of public data for research about automatic spatio-temporal annotations for traffic safety in the roads and to integrate contextual information into conventional Faster R-CNN using Context Mining and Augmented Context Mining to complement the accuracy for small pedestrian detection. The Dataset Year Source Domain #Video #HRA #NHRA #View #Scenario Modality Resolution Variations Subway Entrance [3] 2008 Surveillance Pedestrian 1 5 - 1 1 RGB 512×384 % Subway Exit [3] 2008 Surveillance Pedestrian 1 3 - 1 1 RGB 512×384 % urban surveillance video dataset (USVD) in details. Surveillance Video Datasets The majority of surveillance video datasets have some lim-itations on the number of videos or the degree of reality, such as UCSD Ped1 [20], UCSD Ped2 datasets [20], Av-enue dataset [25], Subway dataset [2], ShanghaiTech Cam-pus dataset [26], NWPU [5], etc. The proposed system consists mainly of two modules, first one responsible of vehicle detection and classification and a second one for vehicle tracking. For the dynamic database, we collected vehicle images from different road environments using road surveillance cameras in conjunction with the traffic administration department. 266/VVC, AV1, H. The proposed system compares the fight detection Sep 13, 2022 · The dataset includes ground truth annotations for all common road objects in JSON format, lane markings, pixel-wise semantic segmentation, instance segmentation, panoptic segmentation and even pose-estimation labels. The testing set contains 1,804 images in three video clips. Jan 1, 2022 · EfficientNet: Rethinking model scaling for convolutional neural networks. A key factor in defining activity is the temporal length or duration of the activity. The resulting regions are finally fed into a three-dimensional Convolutional Set of video-based and multimodal traffic surveillance datasets. Biomedical Research-India. Promptly detecting such obstacles on the road is of great significance for driving safety and Intelligent Transportation Systems (ITS). [36] (the UCF-Crime dataset) contains a large set of internet videos taken from hundredsofdifferentcameras. To cope with the scarcity of real-world data, this paper utilizes synthetic road surveillance data to train a YOLOv5 model for detecting road events from surveillance videos. In their formulation, both anoma-lous and normal video is given for training. In this project a video dataset is built and made public so that researchers can evaluate their Synthetically Generated Surveillance Perspective Human Action Recognition Dataset: 6901 Videos from 10 action classes, made by a 3D Simulation, all cropped spatio-temporally and filmed from a surveillance-camera like position. than Mar 29, 2022 · The current concept of smart cities influences urban planners and researchers to provide modern, secured and sustainable infrastructure and gives a decent quality of life to its residents. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies including Abuse, Arrest, Arson, Assault, Road Accident, Burglary, Explosion, Fighting, Robbery, Shooting, Stealing, Shoplifting Surveillance Perspective Human Action Recognition Dataset: 7759 Videos from 14 Action Classes, aggregated from multiple sources, all cropped spatio-temporally and filmed from a surveillance-camera like position. In this project, a video dataset is built and made public so that researchers can evaluate their algorithms on it and is believed to be the first of its kind. mp4 format (330 normal, 366 abnormal, 100 testing). Aug 31, 2022 · In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. 1016/j. Current research primarily focuses on developing diverse road anomaly detection approaches to discriminate the unknown objects regarded as abandoned In the current AI field, there is a significant gap in research on multimodal surveillance video datasets, despite their potential for contributing to social security and daily life. Aug 10, 2021 · In the early mornings and the late nights of winter season, the majority of road accidents occur every year despite less traffic due to the poor visibility of drivers in the presence of fog. This framework’s superiority over the top-notch approaches was demonstrated and also its effectiveness was validated. - yajunbaby/A-Benchmark-for-Road-Abandoned-Object-Detection-from-Video-Surveillance In this paper, we propose a new comprehensive Video Surveillance Quality Assessment Dataset (VSQuAD) dedicated to Video Surveillance (VS) systems. Special interest on intersection surveillance. However, the unavailability of benchmark dataset of real traffic videos is the major bottleneck in doing research. From the image acquisition point of view, the traffic image dataset can be divided into three categories: images taken by the car camera, images taken by the Feb 20, 2022 · 5) Model-5: This model is a target model trained by the merged datasets of vehicle data in the MS COCO dataset and supplemental dataset without initialisation by the parameters of Model-1. The purpose of this repo is to provide a standard dataset for testing traffic observation software on videos from the Carla Simulator. However, quality degradation in surveillance images (SIs) may affect its performance on vision-based "The goal of a practical anomaly detection system is to timely signal an activity that deviates normal patterns and identify the time window of the occurring anomaly. The Python code in this repo creates a number of standard videos with truly accurate ground truth positions for road users in world coordinates. It consists of long untrimmed surveillance videos which cover 13 realworld anomalies, including Abuse, Arrest, Arson, Assault, Road Accident, Burglary, Explosion, Fighting, Robbery, Shooting, Stealing Feb 19, 2021 · Anomalous activity recognition deals with identifying the patterns and events that vary from the normal stream. Through analysis of CADP dataset, we observed a significant degradation of object detection in pedestrian category in our dataset, due to the object sizes and complexity Vehicle detection is a fundamental challenge in urban traffic surveillance video. Nov 1, 2021 · To develop the advanced self-driving systems, many researchers are focusing to alert all possible traffic risk cases from closed-circuit television (CCTV) and dashboard-mounted cameras. This dataset has been collected from the network of CCTV cameras installed at different squares capturing the scenes of Jul 7, 2020 · In modern intelligent video surveillance systems, automatic anomaly detection through computer vision analytics plays a pivotal role which not only significantly increases monitoring efficiency but also reduces the burden on live monitoring. Real-world anomalous events are far more complex and harder to capture due to diverse human behaviors and a wide range of anomaly types. , 2016. To address this issue, this paper proposes a vision-based vehicle detection and counting system. Most of the video data in these datasets are collected through the in-vehicle camera or online platforms such as YouTube. Surveillance Camera's videos that contain anomalies and normal behaviors. Table 1 provides a summary of pop- We were unable to find any reliable datasets, and so we created our own dataset of around 400 extracted frames from a combination of real-world CCTV camera footage and photo-realistic video-game footage. The dataset Videos of fight footage separated into CCTV/Non-CCTV Apr 26, 2019 · Multiple-object tracking and behavior analysis have been the essential parts of surveillance video analysis for public security and urban management. Real time fight detection from surveillance videos will help in preventing or stopping the fight. Numerous benchmark video datasets were taken for AD. In this video-surveillance context, anomalies occur only for a very short time, and very occasionally. of visual surveillance applied to road accident detection. The VIRAT Video Dataset is designed to be realistic, natural and challenging for video surveillance domains in terms of its resolution, background clutter, diversity in scenes, and human activity/event categories than existing action recognition datasets. Hence, manual monitoring of such anomalies may be exhaustive and monotonous, resulting in a decrease in reliability and speed in emergency situations due to monitor tiredness. This dataset was later doubled since we introduced rotational invariance into our model by horizontally flipping our initial 400 images. Hence, we propose a novel approach for detecting and summarizing suspicious activities in surveillance videos. Existing methods are limited to detecting and classifying the predefined events with unsatisfactory semantic understanding, although they have obtained We also introduce a new large-scale first of its kind dataset of 128 hours of videos. However, most of the surveillance videos are low frame rated and extracting the right motion feature from them is a challenging task. Details of IITH road accident dataset. Apr 26, 2019 · This paper introduces an urban surveillance video dataset (USVD) which is by far the largest and most comprehensive. Official project page of the paper "Towards Surveillance Video-and-Language Understanding: New Dataset, Baselines, and Challenges" (Accepted by CVPR 2024) - Xuange923/Surveillance-Video-Understanding Nov 1, 2022 · We have evaluated the proposed framework on a dataset of 80 video sequences consisting of a total of approximately 450 natural driving scenes of car crashes and abnormal driving events captured by traffic surveillance cameras. 1. This is a difficult task, but it is important to automate, improve, and lower the cost of the detection of crimes and other accidents. We modify a pre-existing approach for this task by Jun 6, 2019 · Big data applications are consuming most of the space in industry and research area. Jun 29, 2021 · The dataset for this stage was obtained from the traffic surveillance video. To achieve this goal, many researchers have conducted in-depth research on online video anomaly Although there are well established object detection methods based on static images, their application to video data on a frame by frame basis faces two shortcomings: (i) lack of computational efficiency due to redundancy across image frames or by not using a temporal and spatial correlation of features across image frames, and (ii) lack of robustness to real-world conditions such as motion road datasets play a critical role in advancing and enhanc-ing traffic monitoring systems. Our dataset is specifically designed for the surveillance domain, featuring the longest average word count per sentence. In recent years, designing and testing video anomaly detection methods have focused on synthetic or unrealistic sequences. Due to the powerful representation ability of convolution neural network (CNN), CNN-based detection approaches Jan 7, 2024 · Notably, most datasets in this domain, particularly for accident detection and autonomous vehicle research, are derived from sources like dashcams, traffic surveillance cameras, drones (such as the HighD, InD, or Interaction datasets [30, 31]), and building-mounted cameras. Due to the rapid development of deep learning algorithms in recent years, automatic Video Anomaly Dection Dataset UCF-Crime dataset is a new large-scale first of its kind dataset of 128 hours of videos. Jul 24, 2022 · Detecting suspicious activities in surveillance videos is a longstanding problem in real-time surveillance that leads to difficulties in detecting crimes. 06. Dec 21, 2022 · Autonomous video surveillance becomes a fundamental cornerstone in law enforcement, smart monitoring, road safety, environmental monitoring etc. Also, some non-fight sequences from regular surveillance camera videos are included. In a surveillance paradigm, these events range from abuse to fighting and road accidents to snatching, etc. urban surveillance video dataset (USVD) in details. The resulting depth videos feature people performing a set of different predefined actions, for which we provide detailed annotations. Normal events in this dataset are walking pedestrians, and abnormal events are bikers, wheelchairs, skaters, and joggers. More information about the datasets can be found in the following papers. Quality and Fairness Assurance dataset provides additional samples for these objects from traffic CCTV footage. To fulfill this need, video surveillance cameras have been deployed to enhance the safety and well-being of the citizens. 66x longer than the dataset from [2]. It has been considered an inseparable part of crime prevention, smart cities, medical health care, aviation, and natural disasters. Dec 19, 2021 · We also conducted experiments on the UCSD traffic video dataset. V. Extract C3D feature of video using Google Colab (this jupyter notebook) The VIRAT Video Dataset. These videos have been artificially degraded with various types of distortions (single 2. However, due to the different sizes of vehicles, their detection remains a challenge that directly affects the accuracy of vehicle counts. We collect between 10,000 and 15,000 frames from each of its 10 traffic video sources. Feb 14, 2022 · Compared with traditional vehicle-based video, surveillance video can capture richer road and vehicle information, which will have a large impact on pedestrian cross-street behavior. CCD is distinguished from existing datasets for diversified accident annotations, including environmental attributes (day/night, snowy/rainy/good weather conditions detection by automatic video surveillance cameras on roads, highways, and pedestrian walkways. qrfzvm tjn xsgys zgu sdqvny rhs mvevtvl yobiix kdfx flvw