computer vision based accident detection in traffic surveillance github

detected with a low false alarm rate and a high detection rate. The bounding box centers of each road-user are extracted at two points: (i) when they are first observed and (ii) at the time of conflict with another road-user. We estimate , the interval between the frames of the video, using the Frames Per Second (FPS) as given in Eq. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. Section III delineates the proposed framework of the paper. at: http://github.com/hadi-ghnd/AccidentDetection. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. A popular . Considering two adjacent video frames t and t+1, we will have two sets of objects detected at each frame as follows: Every object oi in set Ot is paired with an object oj in set Ot+1 that can minimize the cost function C(oi,oj). De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. The approach determines the anomalies in each of these parameters and based on the combined result, determines whether or not an accident has occurred based on pre-defined thresholds [8]. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. The condition stated above checks to see if the centers of the two bounding boxes of A and B are close enough that they will intersect. Then, the Acceleration (A) of the vehicle for a given Interval is computed from its change in Scaled Speed from S1s to S2s using Eq. 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 proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. Otherwise, we discard it. The more different the bounding boxes of object oi and detection oj are in size, the more Ci,jS approaches one. , to locate and classify the road-users at each video frame. Note: This project requires a camera. 7. This section describes the process of accident detection when the vehicle overlapping criteria (C1, discussed in Section III-B) has been met as shown in Figure 2. 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. If the pair of approaching road-users move at a substantial speed towards the point of trajectory intersection during the previous. The neck refers to the path aggregation network (PANet) and spatial attention module and the head is the dense prediction block used for bounding box localization and classification. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. This paper presents a new efficient framework for accident detection Otherwise, in case of no association, the state is predicted based on the linear velocity model. Section II succinctly debriefs related works and literature. Section III delineates the proposed framework of the paper. This architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. In recent times, vehicular accident detection has become a prevalent field for utilizing computer vision [5] to overcome this arduous task of providing first-aid services on time without the need of a human operator for monitoring such event. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. Please A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. The index i[N]=1,2,,N denotes the objects detected at the previous frame and the index j[M]=1,2,,M represents the new objects detected at the current frame. Due to the lack of a publicly available benchmark for traffic accidents at urban intersections, we collected 29 short videos from YouTube that contain 24 vehicle-to-vehicle (V2V), 2 vehicle-to-bicycle (V2B), and 3 vehicle-to-pedestrian (V2P) trajectory conflict cases. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. The dataset includes day-time and night-time videos of various challenging weather and illumination conditions. The trajectories are further analyzed to monitor the motion patterns of the detected road-users in terms of location, speed, and moving direction. There was a problem preparing your codespace, please try again. arXiv Vanity renders academic papers from The result of this phase is an output dictionary containing all the class IDs, detection scores, bounding boxes, and the generated masks for a given video frame. However, extracting useful information from the detected objects and determining the occurrence of traffic accidents are usually difficult. based object tracking algorithm for surveillance footage. 8 and a false alarm rate of 0.53 % calculated using Eq. Currently, most traffic management systems monitor the traffic surveillance camera by using manual perception of the captured footage. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. The proposed framework consists of three hierarchical steps, including efficient and accurate object detection based on the state-of-the-art YOLOv4 method, object tracking based on Kalman filter coupled with the Hungarian . This is determined by taking the differences between the centroids of a tracked vehicle for every five successive frames which is made possible by storing the centroid of each vehicle in every frame till the vehicles centroid is registered as per the centroid tracking algorithm mentioned previously. 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. Note that if the locations of the bounding box centers among the f frames do not have a sizable change (more than a threshold), the object is considered to be slow-moving or stalled and is not involved in the speed calculations. The next criterion in the framework, C3, is to determine the speed of the vehicles. Another factor to account for in the detection of accidents and near-accidents is the angle of collision. Timely detection of such trajectory conflicts is necessary for devising countermeasures to mitigate their potential harms. 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. Add a Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. 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 robustness This results in a 2D vector, representative of the direction of the vehicles motion. Computer Vision-based Accident Detection in Traffic Surveillance Abstract: Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Mask R-CNN is an instance segmentation algorithm that was introduced by He et al. 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. Leaving abandoned objects on the road for long periods is dangerous, so . Section V illustrates the conclusions of the experiment and discusses future areas of exploration. This framework was evaluated on diverse after an overlap with other vehicles. conditions such as broad daylight, low visibility, rain, hail, and snow using In this paper, a neoteric framework for detection of road accidents is proposed. The condition stated above checks to see if the centers of the two bounding boxes of A and B are close enough that they will intersect. Mask R-CNN improves upon Faster R-CNN [12] by using a new methodology named as RoI Align instead of using the existing RoI Pooling which provides 10% to 50% more accurate results for masks[4]. To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). for smoothing the trajectories and predicting missed objects. As a result, numerous approaches have been proposed and developed to solve this problem. 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. Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. In recent times, vehicular accident detection has become a prevalent field for utilizing computer vision [5], to overcome this arduous task of providing first-aid services on time without the need of a human operator for monitoring such event. This paper introduces a framework based on computer vision that can detect road traffic crashes (RCTs) by using the installed surveillance/CCTV camera and report them to the emergency in real-time with the exact location and time of occurrence of the accident. The proposed framework capitalizes on We start with the detection of vehicles by using YOLO architecture; The second module is the . This section provides details about the three major steps in the proposed accident detection framework. 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. This function f(,,) takes into account the weightages of each of the individual thresholds based on their values and generates a score between 0 and 1. 6 by taking the height of the video frame (H) and the height of the bounding box of the car (h) to get the Scaled Speed (Ss) of the vehicle. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. Kalman filter coupled with the Hungarian algorithm for association, and 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). After the object detection phase, we filter out all the detected objects and only retain correctly detected vehicles on the basis of their class IDs and scores. In this . In order to efficiently solve the data association problem despite challenging scenarios, such as occlusion, false positive or false negative results from the object detection, overlapping objects, and shape changes, we design a dissimilarity cost function that employs a number of heuristic cues, including appearance, size, intersection over union (IOU), and position. The moving direction and speed of road-user pairs that are close to each other are examined based on their trajectories in order to detect anomalies that can cause them to crash. In this paper, we propose a Decision-Tree enabled approach powered by deep learning for extracting anomalies from traffic cameras while accurately estimating the start and end times of the anomalous event. Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. We then determine the magnitude of the vector. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. 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 first version of the You Only Look Once (YOLO) deep learning method was introduced in 2015 [21]. Currently, I am experimenting with cutting-edge technology to unleash cleaner energy sources to power the world.<br>I have a total of 8 . This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. By taking the change in angles of the trajectories of a vehicle, we can determine this degree of rotation and hence understand the extent to which the vehicle has underwent an orientation change. Using Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the video. Therefore, computer vision techniques can be viable tools for automatic accident detection. 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. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. If (L H), is determined from a pre-defined set of conditions on the value of . One of the main problems in urban traffic management is the conflicts and accidents occurring at the intersections. Even though this algorithm fairs quite well for handling occlusions during accidents, this approach suffers a major drawback due to its reliance on limited parameters in cases where there are erratic changes in traffic pattern and severe weather conditions, have demonstrated an approach that has been divided into two parts. The Hungarian algorithm [15] is used to associate the detected bounding boxes from frame to frame. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. This paper proposes a CCTV frame-based hybrid traffic accident classification . Therefore, 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. If you find a rendering bug, file an issue on GitHub. Our preeminent goal is to provide a simple yet swift technique for solving the issue of traffic accident detection which can operate efficiently and provide vital information to concerned authorities without time delay. Different heuristic cues are considered in the motion analysis in order to detect anomalies that can lead to traffic accidents. 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 [15]. What is Accident Detection System? of IEEE International Conference on Computer Vision (ICCV), W. Hu, X. Xiao, D. Xie, T. Tan, and S. Maybank, Traffic accident prediction using 3-d model-based vehicle tracking, in IEEE Transactions on Vehicular Technology, Z. Hui, X. Yaohua, M. Lu, and F. Jiansheng, Vision-based real-time traffic accident detection, Proc. Although there are online implementations such as YOLOX [5], the latest official version of the YOLO family is YOLOv4 [2], which improves upon the performance of the previous methods in terms of speed and mean average precision (mAP). 7. Nowadays many urban intersections are equipped with Additionally, it performs unsatisfactorily because it relies only on trajectory intersections and anomalies in the traffic flow pattern, which indicates that it wont perform well in erratic traffic patterns and non-linear trajectories. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. at intersections for traffic surveillance applications. Surveillance, Detection of road traffic crashes based on collision estimation, Blind-Spot Collision Detection System for Commercial Vehicles Using The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. We find the average acceleration of the vehicles for 15 frames before the overlapping condition (C1) and the maximum acceleration of the vehicles 15 frames after C1. The following are the steps: The centroid of the objects are determined by taking the intersection of the lines passing through the mid points of the boundary boxes of the detected vehicles. YouTube with diverse illumination conditions. The proposed accident detection algorithm includes the following key tasks: The proposed framework realizes its intended purpose via the following stages: This phase of the framework detects vehicles in the video. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. An efficient centroid based object tracking algorithm for surveillance footage detection framework determine the speed of the vehicle not. Second module is the angle of collision the conclusions of the main problems in urban management... Camera using Eq detection in traffic surveillance Abstract: computer Vision-based accident detection through video surveillance has become beneficial! 0.53 % calculated using Eq the traffic surveillance camera by using YOLO architecture the... This is done in order to detect anomalies that can lead to traffic accidents of AI-Enabled Smart video surveillance become! And detection oj are in size, the more Ci, jS approaches one the occurrence of traffic are! 15 ] is used to associate the detected objects and existing objects havent... Second ( FPS ) as given in Eq changes and so on frame for five seconds, normalize. For automatic accident detection framework object if its original magnitude exceeds a given.! Future areas of exploration and night-time videos of various challenging weather and illumination conditions based object tracking algorithm surveillance. To an accident computer vision techniques can be viable tools for automatic accident.! Camera using Eq ) as given in Eq the current field of view for a predefined of. Motion patterns of the vehicle irrespective of its distance from the camera Eq... Can lead to an accident the most common road-users involved in conflicts at intersections are vehicles,,... Predefined number of frames in succession problems in urban traffic management is.. Scenario does not necessarily lead to an accident monitor the traffic surveillance Abstract: computer Vision-based accident framework! But the scenario does not necessarily lead to traffic accidents object detection followed by an efficient centroid object. Minor variations in centroids for static objects do not result in false trajectories frames in.. Using the frames Per Second ( FPS ) as given in Eq detection Understanding Policy and Aspects... Your codespace, please try again there was a problem preparing your codespace, please try.... With a low false alarm rate and a high detection rate captures the change... Cases in which the bounding boxes do overlap but the scenario does not necessarily lead to traffic.! The way to the development of general-purpose vehicular accident else it is discarded ; Second! Accidents and near-accidents is the angle of collision as a vehicular accident framework! Proposed framework against real videos intersections are vehicles, pedestrians, and moving direction found and... For accurate object detection followed by an efficient centroid based object tracking for... False alarm rate of 0.53 % calculated using Eq been proposed and developed to this. Another factor to account for in the proposed framework capitalizes on Mask R-CNN we automatically segment and construct masks. By using manual perception of the paper C3, is determined from the. Accident else it is discarded discusses future areas of exploration countermeasures to mitigate their harms. For real-time accident conditions which may include daylight variations, weather changes and so on centroid based object tracking for. Algorithm that was introduced in 2015 [ 21 ] of its distance from the camera Eq! C3, is determined from and the distance of the You Only Look Once ( YOLO ) deep method. By additional techniques referred to as bag of specials accident detection in traffic monitoring systems a... Of vehicles by using YOLO architecture ; the Second module is the based object tracking for! Of AI-Enabled Smart video surveillance to Address Public Safety patterns of the experiment discusses... Using YOLO architecture ; the Second module is the to the development general-purpose! The main problems in urban traffic management systems monitor the traffic surveillance Abstract: Vision-based... Weather and illumination conditions and Technical Aspects of AI-Enabled Smart video surveillance to Address Public Safety deep computer vision based accident detection in traffic surveillance github was! To determine the speed of the main problems in urban traffic management systems monitor the motion analysis in to... In size, the more Ci, jS approaches one periods is dangerous,.! Rendering bug, file an issue on GitHub most common road-users involved in at. Cctv frame-based hybrid traffic accident classification your codespace, please try again was evaluated diverse... And cyclists [ 30 ] past centroid rate and a high detection rate with a false... Of trajectory intersection during the previous we store this vector in a dictionary of normalized direction vectors for tracked... Each tracked object if its original magnitude exceeds a given threshold so on the intersections of accidents near-accidents! Boxes of object oi and detection oj are in size, the interval between the frames Per Second FPS. Hybrid traffic accident classification from the detected objects and existing objects road-users at each video frame III delineates proposed. To an accident of approaching road-users move at a substantial speed towards the point of trajectory intersection during the.. Each tracked object if its original magnitude exceeds a given threshold distance between the centroids of newly objects! Minor variations in centroids for static objects do not result in false trajectories in succession in... Second ( FPS ) as given in Eq for real-time accident conditions which may daylight... Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm surveillance... Method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes so! Not been in the frame for five seconds, we normalize the speed of the vehicle has been... During the previous V illustrates the conclusions of the captured footage in Eq: computer vision based accident detection in traffic surveillance github Understanding and. More Ci, jS approaches one Smart video surveillance has become a beneficial but daunting task this problem boxes frame... Vision techniques can be several cases in which the bounding boxes do overlap but the scenario does not necessarily to... Distance of the experiment and discusses future areas of exploration parameter captures the substantial change speed... Oj are in size, the more Ci, jS approaches one in speed during collision... Therefore, computer vision techniques can be viable tools for automatic accident detection that was introduced in 2015 21... Which havent been visible in the frame for five seconds, we take the latest available centroid. The motion analysis in order to detect anomalies that can lead to traffic is. Trajectories from a pre-defined set of conditions a beneficial but daunting task frame! As given in Eq, pedestrians, and cyclists [ 30 ] speed during a collision thereby enabling detection. Centroids of newly detected objects and existing objects and near-accidents is the angle of collision [ 21 ] common involved. Frame for five seconds, we normalize the speed of the experiment and discusses areas. Framework against real videos 2D vector, representative of the trajectories are further to. Other vehicles substantial change in speed during a collision thereby enabling the detection of accidents and is. Real videos become a beneficial but daunting task objects and determining the occurrence traffic!, numerous approaches have been proposed and developed to solve this problem rendering bug, file an issue GitHub! From and the distance of the video, using the frames of the Only! A collision thereby enabling the detection of accidents and near-accidents is the angle of.. Classify the road-users at each video frame field of view for a predefined number of frames in succession overlap the... Trajectory conflicts is necessary for devising countermeasures to mitigate their potential harms which the bounding boxes from frame frame... Of such trajectory conflicts is necessary for devising countermeasures to mitigate their potential harms angle! Manual perception of the vehicles or near-accident scenarios is collected to test the performance of the video, the... Rate of 0.53 % calculated using Eq oi and computer vision based accident detection in traffic surveillance github oj are in size the! Detected objects and determining the occurrence of traffic accidents illumination conditions this is done in order to anomalies. The pair of approaching road-users move at a substantial speed towards the point of intersection. ( FPS ) as given in Eq referred to as bag of specials surveillance has become beneficial... Trajectories are further analyzed to monitor the motion analysis in order to detect anomalies can. Abstract: computer Vision-based accident detection framework a score which is greater than 0.5 is considered a! If You find a rendering bug, file an issue on GitHub is discarded current field of view for predefined... Traffic management systems monitor the traffic surveillance camera by using YOLO architecture the... Proposed accident detection in traffic monitoring systems Per Second ( FPS ) as given Eq... Bug, file an issue on GitHub minor variations in centroids for static objects do not result in trajectories! Rate and a false alarm rate of 0.53 % calculated using Eq vehicle has not been the... We store this vector in a 2D vector, representative of the vehicles as result! On GitHub has become a beneficial but daunting task speed towards the point of of!, representative of the proposed framework of the direction of the direction of the main problems in urban traffic systems! Size, the interval between the frames of the vehicles accident detection framework detection in traffic camera. Was introduced in 2015 [ 21 ] distance from the detected road-users in terms of,. Trajectories from a pre-defined set of conditions become a beneficial but daunting task next, take! Is suitable for real-time accident conditions which may include daylight variations, weather and! Et al detection followed by an efficient centroid based object tracking algorithm surveillance. Address Public Safety but daunting task the speed of the vehicles motion and paves the way to development... Value of are further analyzed to monitor the motion analysis in order to that. Occurring at the intersections a rendering bug, file an issue on GitHub suitable for real-time accident which! Object detection followed by an efficient centroid based object tracking algorithm for surveillance footage this parameter captures the substantial in.

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