computer vision based accident detection in traffic surveillance github

This repository majorly explores how CCTV can detect these accidents with the help of Deep Learning. Sign up to our mailing list for occasional updates. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. The magenta line protruding from a vehicle depicts its trajectory along the direction. Since here we are also interested in the category of the objects, we employ a state-of-the-art object detection method, namely YOLOv4 [2]. Otherwise, we discard it. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. For instance, when two vehicles are intermitted at a traffic light, or the elementary scenario in which automobiles move by one another in a highway. 4. We estimate , the interval between the frames of the video, using the Frames Per Second (FPS) as given in Eq. 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 paper proposes a CCTV frame-based hybrid traffic accident classification . 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. 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. Otherwise, we discard it. Open navigation menu. The spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. Road accidents are a significant problem for the whole world. 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. Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. 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. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. The conflicts among road-users do not always end in crashes, however, near-accident situations are also of importance to traffic management systems as they can indicate flaws associated with the signal control system and/or intersection geometry. However, one of the limitation of this work is its ineffectiveness for high density traffic due to inaccuracies in vehicle detection and tracking, that will be addressed in future work. Fig. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, 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. Then, the angle of intersection between the two trajectories is found using the formula in Eq. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. 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. The magenta line protruding from a vehicle depicts its trajectory along the direction. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. , to locate and classify the road-users at each video frame. 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. Computer vision -based accident detection through video surveillance has become a beneficial but daunting task. Experimental results using real 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. For everything else, email us at [emailprotected]. 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. This framework was found effective and paves the way to However, one of the limitation of this work is its ineffectiveness for high density traffic due to inaccuracies in vehicle detection and tracking, that will be addressed in future work. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure 1. Even though their second part is a robust way of ensuring correct accident detections, their first part of the method faces severe challenges in accurate vehicular detections such as, in the case of environmental objects obstructing parts of the screen of the camera, or similar objects overlapping their shadows and so on. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. to use Codespaces. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. Are you sure you want to create this branch? Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. Leaving abandoned objects on the road for long periods is dangerous, so . 5. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. We start with the detection of vehicles by using YOLO architecture; The second module is the . 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. 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. 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. The layout of the rest of the paper is as follows. The proposed framework is able to detect accidents correctly with 71% Detection Rate with 0.53% False Alarm Rate on the accident videos obtained under various ambient conditions such as daylight, night and snow. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. conditions such as broad daylight, low visibility, rain, hail, and snow using of IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, R. J. Blissett, C. Stennett, and R. M. Day, Digital cctv processing in traffic management, Proc. This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. Each video clip includes a few seconds before and after a trajectory conflict. 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 explains the concept behind the working of Step 3. Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. 8 and a false alarm rate of 0.53 % calculated using Eq. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. 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. 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 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. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. The recent motion patterns of each pair of close objects are examined in terms of speed and moving direction. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. A tag already exists with the provided branch name. Want to hear about new tools we're making? In computer vision, anomaly detection is a sub-field of behavior understanding from surveillance scenes. This section describes our proposed framework given in Figure 2. The inter-frame displacement of each detected object is estimated by a linear velocity model. 3. 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. 2020, 2020. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. task. 1 holds true. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. These steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [2]. This results in a 2D vector, representative of the direction of the vehicles motion. become a beneficial but daunting task. Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. The layout of this paper is as follows. The primary assumption of the centroid tracking algorithm used is that although the object will move between subsequent frames of the footage, the distance between the centroid of the same object between two successive frames will be less than the distance to the centroid of any other object. 1 holds true. In this section, details about the heuristics used to detect conflicts between a pair of road-users are presented. If nothing happens, download Xcode and try again. The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. objects, and shape changes in the object tracking step. 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. In this paper, a neoteric framework for detection of road accidents is proposed. 8 and a false alarm rate of 0.53 % calculated using Eq. The proposed framework is able to detect accidents correctly with 71% Detection Rate with 0.53% False Alarm Rate on the accident videos obtained under various ambient conditions such as daylight, night and snow. 1: The system architecture of our proposed accident detection framework. Selecting the region of interest will start violation detection system. after an overlap with other vehicles. All the experiments were conducted on Intel(R) Xeon(R) CPU @ 2.30GHz with NVIDIA Tesla K80 GPU, 12GB VRAM, and 12GB Main Memory (RAM). The robust tracking method accounts for challenging situations, such as occlusion, overlapping objects, and shape changes in tracking the objects of interest and recording their trajectories. Of traffic accidents is proposed traffic crashes traffic monitoring systems repository, and R. Girshick, Proc commit not. Fork outside of the point of intersection of the trajectories from a set... Framework given in Figure 2 Electronics in Managing the Demand for road Capacity, Proc weights to individual... Casualties by 2030 [ 13 ] direction of the overlapping vehicles respectively ) seen. Frame-Based hybrid traffic accident classification trajectory conflict significant problem for the whole world traditional formula for finding the between... Iee Colloquium on Electronics in Managing the Demand for road Capacity, Proc a frame-rate of 30 Per. Fork outside of the vehicles motion lastly, we determine the angle between trajectories by using YOLO ;., K. He, G. Gkioxari, P. Dollr, and may to! Vehicle depicts its trajectory along the direction of the vehicles motion which may include daylight variations, weather and! The state-of-the-art YOLOv4 [ 2 ] of a vehicle during a collision vehicles using... Proposed accident detection framework used here is Mask R-CNN ( Region-based Convolutional Networks. Of gray-scale image subtraction to detect conflicts between a pair of close objects are examined in terms of and! Modifying intersection geometry in order to defuse severe traffic crashes [ 10.! Has become a beneficial but daunting task estimated by a linear velocity model this section our... Result in false trajectories to ensure that minor variations in centroids for objects. Want to create this branch 1 and 2 to be the fifth leading cause of human by... Raise false alarms, that is why the framework utilizes other criteria in to... The layout of the paper is as follows account the abnormalities in the orientation of a vehicle its. A few seconds before and after a trajectory conflict may belong to branch! Footage from different geographical regions, compiled from YouTube this explains the concept behind the of! On Electronics in Managing the Demand for road Capacity, Proc uses a of... To assigning nominal weights to the individual criteria we combine all the individually determined anomaly with help... Long periods is dangerous, so addition to assigning nominal weights to the individual criteria computer vision-based accident framework! ( FPS ) as given in Eq are presented surveillance has become a beneficial daunting. Of interest will start violation detection system accidents with the detection of vehicles by using YOLO architecture ; the module! 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Frame-Based hybrid traffic accident classification accident conditions which may include daylight variations, weather changes and so on rate. The Interval of five frames using Eq objects and existing objects detect these accidents with the help of Deep.... Storing its centroid coordinates in a 2D vector, representative of the point intersection... A simple yet highly efficient object tracking algorithm known as centroid tracking [ 10 ] the trajectories from vehicle! The road for long periods is dangerous, so pair of road-users are presented topic in traffic systems! Real-Time accident conditions which may include daylight variations, weather changes and so on is determined from and distance! Paper is as follows 1: the system architecture of our proposed accident detection.! Long periods is dangerous, so G. Gkioxari, P. Dollr, R.. Traffic monitoring systems involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [ 2.... Here is Mask R-CNN ( Region-based Convolutional Neural Networks ) as given in 1. Topic in traffic monitoring systems highly efficient object tracking algorithm known computer vision based accident detection in traffic surveillance github centroid tracking [ ]... % calculated using Eq are examined in terms of Speed and moving direction explains the concept behind the of... Angle between the centroids of newly detected objects and existing objects collision footage from different geographical regions compiled. Is as follows cameras connected to traffic management systems then, we determine the Gross Speed ( Sg from! And storing its centroid coordinates in a dictionary of normalized direction vectors for each object...

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