We start with the detection of vehicles by using YOLO architecture; The second module is the . In this paper, a neoteric framework for 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. We determine the speed of the vehicle in a series of steps. 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. Pawar K. and Attar V., " Deep learning based detection and localization of road accidents from traffic surveillance videos," ICT Express, 2021. 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. What is Accident Detection System? Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. We can minimize this issue by using CCTV accident detection. One of the solutions, proposed by Singh et al. 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. Numerous studies have applied computer vision techniques in traffic surveillance systems [26, 17, 9, 7, 6, 25, 8, 3, 10, 24] for various tasks. A predefined number (B. ) 3. These object pairs can potentially engage in a conflict and they are therefore, chosen for further analysis. We used a desktop with a 3.4 GHz processor, 16 GB RAM, and an Nvidia GTX-745 GPU, to implement our proposed method. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. accident detection by trajectory conflict analysis. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. A classifier is trained based on samples of normal traffic and traffic accident. Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. 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. Consider a, b to be the bounding boxes of two vehicles A and B. Section IV contains the analysis of our experimental results. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 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 incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. 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]. 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 next task in the framework, T2, is to determine the trajectories of the vehicles. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. As illustrated in fig. Based on this angle for each of the vehicles in question, we determine the Change in Angle Anomaly () based on a pre-defined set of conditions. The proposed framework consists of three hierarchical steps, including . We then normalize this vector by using scalar division of the obtained vector by its magnitude. Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. 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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 Computer Vision-based Accident Detection in Traffic Surveillance Earnest Paul Ijjina, Dhananjai Chand, Savyasachi Gupta, Goutham K Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. Road accidents are a significant problem for the whole world. 5. Additionally, despite all the efforts in preventing hazardous driving behaviors, running the red light is still common. Use Git or checkout with SVN using the web URL. The probability of an accident is . 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. A popular . Are you sure you want to create this branch? applied for object association to accommodate for occlusion, overlapping Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. The velocity components are updated when a detection is associated to a target. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. You can also use a downloaded video if not using a camera. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. 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. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. traffic video data show the feasibility of the proposed method in real-time The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. Anomalies are typically aberrations of scene entities (people, vehicles, environment) and their interactions from normal behavior. We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. 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. 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. Open navigation menu. 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 inter-frame displacement of each detected object is estimated by a linear velocity model. One of the solutions, proposed by Singh et al. Section II succinctly debriefs related works and literature. A sample of the dataset is illustrated in Figure 3. 3. Section III delineates the proposed framework of the paper. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. Typically, anomaly detection methods learn the normal behavior via training. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. The trajectories are further analyzed to monitor the motion patterns of the detected road-users in terms of location, speed, and moving direction. Samples of normal traffic and traffic accident step in the framework utilizes other criteria in addition to nominal. And YouTube for availing the videos used in this dataset the detected, masked vehicles, could! 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