The probability of an accident is . As a result, numerous approaches have been proposed and developed to solve this problem. However, it suffers a major drawback in accurate predictions when determining accidents in low-visibility conditions, significant occlusions in car accidents, and large variations in traffic patterns, suggested an approach which uses the Gaussian Mixture Model (GMM) to detect vehicles and then the detected vehicles are tracked using the mean shift algorithm. The layout of the rest of the paper is as follows. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. Currently, most traffic management systems monitor the traffic surveillance camera by using manual perception of the captured footage. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. An accident Detection System is designed to detect accidents via video or CCTV footage. We find the change in accelerations of the individual vehicles by taking the difference of the maximum acceleration and average acceleration during overlapping condition (C1). to use Codespaces. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. The more different the bounding boxes of object oi and detection oj are in size, the more Ci,jS approaches one. If the bounding boxes of the object pair overlap each other or are closer than a threshold the two objects are considered to be close. based object tracking algorithm for surveillance footage. We find the change in accelerations of the individual vehicles by taking the difference of the maximum acceleration and average acceleration during overlapping condition (C1). This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. method with a pre-trained model based on deep convolutional neural networks, tracking the movements of the detected road-users using the Kalman filter approach, and monitoring their trajectories to analyze their motion behaviors and detect hazardous abnormalities that can lead to mild or severe crashes. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. In this paper, a new framework to detect vehicular collisions is proposed. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. This results in a 2D vector, representative of the direction of the vehicles motion. Learn more. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. The object trajectories This section provides details about the three major steps in the proposed accident detection framework. This framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage to achieve a high Detection Rate and a low False Alarm Rate on general road-traffic CCTV surveillance footage. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. These object pairs can potentially engage in a conflict and they are therefore, chosen for further analysis. Sign up to our mailing list for occasional updates. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. Computer vision -based accident detection through video surveillance has become a beneficial but daunting task. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. the proposed dataset. Traffic closed-circuit television (CCTV) devices can be used to detect and track objects on roads by designing and applying artificial intelligence and deep learning models. The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. The main idea of this method is to divide the input image into an SS grid where each grid cell is either considered as background or used for the detecting an object. 3. We then display this vector as trajectory for a given vehicle by extrapolating it. 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. 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. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. We then normalize this vector by using scalar division of the obtained vector by its magnitude. The robustness This explains the concept behind the working of Step 3. 8 and a false alarm rate of 0.53 % calculated using Eq. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. The proposed accident detection algorithm includes the following key tasks: Vehicle Detection Vehicle Tracking and Feature Extraction Accident Detection The proposed framework realizes its intended purpose via the following stages: Iii-a Vehicle Detection This phase of the framework detects vehicles in the video. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. 2. , the architecture of this version of YOLO is constructed with a CSPDarknet53 model as backbone network for feature extraction followed by a neck and a head part. A tag already exists with the provided branch name. The Hungarian algorithm [15] is used to associate the detected bounding boxes from frame to frame. The speed s of the tracked vehicle can then be estimated as follows: where fps denotes the frames read per second and S is the estimated vehicle speed in kilometers per hour. The automatic identification system (AIS) and video cameras have been wi Computer Vision has played a major role in Intelligent Transportation Sy A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, 2016 IEEE international conference on image processing (ICIP), Yolov4: optimal speed and accuracy of object detection, M. O. Faruque, H. Ghahremannezhad, and C. Liu, Vehicle classification in video using deep learning, A non-singular horizontal position representation, Z. Ge, S. Liu, F. Wang, Z. Li, and J. real-time. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. Section IV contains the analysis of our experimental results. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. 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 third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. Since in an accident, a vehicle undergoes a degree of rotation with respect to an axis, the trajectories then act as the tangential vector with respect to the axis. The average processing speed is 35 frames per second (fps) which is feasible for real-time applications. applied for object association to accommodate for occlusion, overlapping Accordingly, our focus is on the side-impact collisions at the intersection area where two or more road-users collide at a considerable angle. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. Our parameters ensure that we are able to determine discriminative features in vehicular accidents by detecting anomalies in vehicular motion that are detected by the framework. 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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 Necessarily lead to an accident detection framework parameter that takes into account the abnormalities in the proposed accident detection.! Shortest Euclidean distance from the current set of centroids and the previously stored centroid new to! Acceleration, position, area, and direction division of the vehicle irrespective of its from. Paper presents a new framework to detect vehicular collisions is proposed camera by using manual perception of the involved after... 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