We then utilize the output of the neural network to identify road-side vehicular accidents by extracting feature points and creating our own set of parameters which are then used to identify vehicular accidents. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. the development of general-purpose vehicular accident detection algorithms in In computer vision, anomaly detection is a sub-field of behavior understanding from surveillance scenes. method to achieve a high Detection Rate and a low False Alarm Rate on general This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. Since most intersections are equipped with surveillance cameras automatic detection of traffic accidents based on computer vision technologies will mean a great deal to traffic monitoring systems. applied for object association to accommodate for occlusion, overlapping The next task in the framework, T2, is to determine the trajectories of the vehicles. 9. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. Want to hear about new tools we're making? Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. All the experiments conducted in relation to this framework validate the potency and efficiency of the proposition and thereby authenticates the fact that the framework can render timely, valuable information to the concerned authorities. Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. This framework was evaluated on diverse Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. An accident Detection System is designed to detect accidents via video or CCTV footage. This is the key principle for detecting an accident. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. In the event of a collision, a circle encompasses the vehicles that collided is shown. In addition to the mentioned dissimilarity measures, we also use the IOU value to calculate the Jaccard distance as follows: where Box(ok) denotes the set of pixels contained in the bounding box of object k. The overall dissimilarity value is calculated as a weighted sum of the four measures: in which wa, ws, wp, and wk define the contribution of each dissimilarity value in the total cost function. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. based object tracking algorithm for surveillance footage. In this paper, a neoteric framework for detection of road accidents is proposed. Detection of Rainfall using General-Purpose The framework integrates three major modules, including object detection based on YOLOv4 method, a tracking method based on Kalman filter and Hungarian algorithm with a new cost function, and an accident detection module to analyze the extracted trajectories for anomaly detection. The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. Section III delineates the proposed framework of the paper. The proposed framework provides a robust method to achieve a high Detection Rate and a low False Alarm Rate on general road-traffic CCTV surveillance footage. The object trajectories of bounding boxes and their corresponding confidence scores are generated for each cell. Let x, y be the coordinates of the centroid of a given vehicle and let , be the width and height of the bounding box of a vehicle respectively. Pawar K. and Attar V., " Deep learning based detection and localization of road accidents from traffic surveillance videos," ICT Express, 2021. to detect vehicular accidents used the feed of a CCTV surveillance camera by generating Spatio-Temporal Video Volumes (STVVs) and then extracting deep representations on denoising autoencoders in order to generate an anomaly score while simultaneously detecting moving objects, tracking the objects, and then finding the intersection of their tracks to finally determine the odds of an accident occurring. Therefore, Timely detection of such trajectory conflicts is necessary for devising countermeasures to mitigate their potential harms. Additionally, the Kalman filter approach [13]. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure. Additionally, we plan to aid the human operators in reviewing past surveillance footages and identifying accidents by being able to recognize vehicular accidents with the help of our approach. Before the collision of two vehicular objects, there is a high probability that the bounding boxes of the two objects obtained from Section III-A will overlap. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. First, the Euclidean distances among all object pairs are calculated in order to identify the objects that are closer than a threshold to each other. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. Leaving abandoned objects on the road for long periods is dangerous, so . Similarly, Hui et al. The next criterion in the framework, C3, is to determine the speed of the vehicles. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. This framework was evaluated on. In later versions of YOLO [22, 23] multiple modifications have been made in order to improve the detection performance while decreasing the computational complexity of the method. Use Git or checkout with SVN using the web URL. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. This takes a substantial amount of effort from the point of view of the human operators and does not support any real-time feedback to spontaneous events. Once the vehicles have been detected in a given frame, the next imperative task of the framework is to keep track of each of the detected objects in subsequent time frames of the footage. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. Support vector machine (SVM) [57, 58] and decision tree have been used for traffic accident detection. Dhananjai Chand2, Savyasachi Gupta 3, Goutham K 4, Assistant Professor, Department of Computer Science and Engineering, B.Tech., Department of Computer Science and Engineering, Results, Statistics and Comparison with Existing models, F. Baselice, G. Ferraioli, G. Matuozzo, V. Pascazio, and G. Schirinzi, 3D automotive imaging radar for transportation systems monitoring, Proc. Import Libraries Import Video Frames And Data Exploration The appearance distance is calculated based on the histogram correlation between and object oi and a detection oj as follows: where CAi,j is a value between 0 and 1, b is the bin index, Hb is the histogram of an object in the RGB color-space, and H is computed as follows: in which B is the total number of bins in the histogram of an object ok. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. 2020, 2020. arXiv Vanity renders academic papers from Despite the numerous measures being taken to upsurge road monitoring technologies such as CCTV cameras at the intersection of roads [3] and radars commonly placed on highways that capture the instances of over-speeding cars [1, 7, 2] , many lives are lost due to lack of timely accidental reports [14] which results in delayed medical assistance given to the victims. All the experiments conducted in relation to this framework validate the potency and efficiency of the proposition and thereby authenticates the fact that the framework can render timely, valuable information to the concerned authorities. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. Surveillance Cameras, https://lilianweng.github.io/lil-log/assets/images/rcnn-family-summary.png, https://www.asirt.org/safe-travel/road-safety-facts/, https://www.cdc.gov/features/globalroadsafety/index.html. A classifier is trained based on samples of normal traffic and traffic accident. This results in a 2D vector, representative of the direction of the vehicles motion. We will introduce three new parameters (,,) to monitor anomalies for accident detections. The family of YOLO-based deep learning methods demonstrates the best compromise between efficiency and performance among object detectors. We then display this vector as trajectory for a given vehicle by extrapolating it. A dataset of various traffic videos containing accident or near-accident scenarios is collected to test the performance of the proposed framework against real videos. The performance is compared to other representative methods in table I. The proposed framework capitalizes on As in most image and video analytics systems the first step is to locate the objects of interest in the scene. The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. Computer Vision-based Accident Detection in Traffic Surveillance Abstract: Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Logging and analyzing trajectory conflicts, including severe crashes, mild accidents and near-accident situations will help decision-makers improve the safety of the urban intersections. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Moreover, Ki et al. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure 1. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. to detect vehicular accidents used the feed of a CCTV surveillance camera by generating Spatio-Temporal Video Volumes (STVVs) and then extracting deep representations on denoising autoencoders in order to generate an anomaly score while simultaneously detecting moving objects, tracking the objects, and then finding the intersection of their tracks to finally determine the odds of an accident occurring. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. We estimate the collision between two vehicles and visually represent the collision region of interest in the frame with a circle as show in Figure 4. Video processing was done using OpenCV4.0. Over a course of the precedent couple of decades, researchers in the fields of image processing and computer vision have been looking at traffic accident detection with great interest [5]. detect anomalies such as traffic accidents in real time. Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. As there may be imperfections in the previous steps, especially in the object detection step, analyzing only two successive frames may lead to inaccurate results. This is done for both the axes. Currently, most traffic management systems monitor the traffic surveillance camera by using manual perception of the captured footage. This repository majorly explores how CCTV can detect these accidents with the help of Deep Learning. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. The inter-frame displacement of each detected object is estimated by a linear velocity model. You can also use a downloaded video if not using a camera. Considering the applicability of our method in real-time edge-computing systems, we apply the efficient and accurate YOLOv4 [2] method for object detection. The proposed framework provides a robust method to achieve a high Detection Rate and a low False Alarm Rate on general road-traffic CCTV surveillance footage. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. Sun, Robust road region extraction in video under various illumination and weather conditions, 2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS), A new adaptive bidirectional region-of-interest detection method for intelligent traffic video analysis, A real time accident detection framework for traffic video analysis, Machine Learning and Data Mining in Pattern Recognition, MLDM, Automatic road detection in traffic videos, 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), A new online approach for moving cast shadow suppression in traffic videos, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), E. P. Ijjina, D. Chand, S. Gupta, and K. Goutham, Computer vision-based accident detection in traffic surveillance, 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), A new approach to linear filtering and prediction problems, A traffic accident recording and reporting model at intersections, IEEE Transactions on Intelligent Transportation Systems, The hungarian method for the assignment problem, T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollr, and C. L. Zitnick, Microsoft coco: common objects in context, G. Liu, H. Shi, A. Kiani, A. Khreishah, J. Lee, N. Ansari, C. Liu, and M. M. Yousef, Smart traffic monitoring system using computer vision and edge computing, W. Luo, J. Xing, A. Milan, X. Zhang, W. Liu, and T. Kim, Multiple object tracking: a literature review, NVIDIA ai city challenge data and evaluation, Deep learning based detection and localization of road accidents from traffic surveillance videos, J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You only look once: unified, real-time object detection, Proceedings of the IEEE conference on computer vision and pattern recognition, Anomalous driving detection for traffic surveillance video analysis, 2021 IEEE International Conference on Imaging Systems and Techniques (IST), H. Shi, H. Ghahremannezhadand, and C. Liu, A statistical modeling method for road recognition in traffic video analytics, 2020 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), A new foreground segmentation method for video analysis in different color spaces, 24th International Conference on Pattern Recognition, Z. Tang, G. Wang, H. Xiao, A. Zheng, and J. Hwang, Single-camera and inter-camera vehicle tracking and 3d speed estimation based on fusion of visual and semantic features, Proceedings of the IEEE conference on computer vision and pattern recognition workshops, A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition, L. Yue, M. Abdel-Aty, Y. Wu, O. Zheng, and J. Yuan, In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention, Computer Vision-based Accident Detection in Traffic Surveillance, Artificial Intelligence Enabled Traffic Monitoring System, Incident Detection on Junctions Using Image Processing, Automatic vehicle trajectory data reconstruction at scale, Real-time Pedestrian Surveillance with Top View Cumulative Grids, Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion The next task in the framework, T2, is to determine the trajectories of the vehicles. of International Conference on Systems, Signals and Image Processing (IWSSIP), A traffic accident recording and reporting model at intersections, in IEEE Transactions on Intelligent Transportation Systems, T. Lin, M. Maire, S. J. Belongie, L. D. Bourdev, R. B. Girshick, J. Hays, P. Perona, D. Ramanan, P. Dollr, and C. L. Zitnick, Microsoft COCO: common objects in context, J. C. Nascimento, A. J. Abrantes, and J. S. Marques, An algorithm for centroid-based tracking of moving objects, Proc. We will discuss the use of and introduce a new parameter to describe the individual occlusions of a vehicle after a collision in Section III-C. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Object detection for dummies part 3: r-cnn family, Faster r-cnn: towards real-time object detection with region proposal networks, in IEEE Transactions on Pattern Analysis and Machine Intelligence, Road traffic injuries and deathsa global problem, Deep spatio-temporal representation for detection of road accidents using stacked autoencoder, https://lilianweng.github.io/lil-log/assets/images/rcnn-family-summary.png, https://www.asirt.org/safe-travel/road-safety-facts/, https://www.cdc.gov/features/globalroadsafety/index.html. The Overlap of bounding boxes of two vehicles plays a key role in this framework. 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Containing accident or near-accident scenarios is collected to test the performance of the trajectories computer vision based accident detection in traffic surveillance github...
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