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. Therefore, computer vision techniques can be viable tools for automatic accident detection. 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. Surveillance, Detection of road traffic crashes based on collision estimation, Blind-Spot Collision Detection System for Commercial Vehicles Using The framework is built of five modules. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. We can minimize this issue by using CCTV accident detection. 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. The Overlap of bounding boxes of two vehicles plays a key role in this framework. The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. We then determine the magnitude of the vector, , as shown in Eq. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. 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. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. 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. As a result, numerous approaches have been proposed and developed to solve this problem. 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. A predefined number (B. ) The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: 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. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. 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 section describes our proposed framework given in Figure 2. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. Edit social preview. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. One of the main problems in urban traffic management is the conflicts and accidents occurring at the intersections. The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. sign in The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. In the event of a collision, a circle encompasses the vehicles that collided is shown. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. The existing approaches are optimized for a single CCTV camera through parameter customization. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. 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. They do not perform well in establishing standards for accident detection as they require specific forms of input and thereby cannot be implemented for a general scenario. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Google Scholar [30]. Then, we determine the distance covered by a vehicle over five frames from the centroid of the vehicle c1 in the first frame and c2 in the fifth frame. YouTube with diverse illumination conditions. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. 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. 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 existing approaches are optimized for a single CCTV camera through parameter customization. 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. Then, we determine the distance covered by a vehicle over five frames from the centroid of the vehicle c1 in the first frame and c2 in the fifth frame. The magenta line protruding from a vehicle depicts its trajectory along the direction. In the event of a collision, a circle encompasses the vehicles that collided is shown. 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. Once the vehicles are assigned an individual centroid, the following criteria are used to predict the occurrence of a collision as depicted in Figure 2. 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. Mask R-CNN is an instance segmentation algorithm that was introduced by He et al. method to achieve a high Detection Rate and a low False Alarm Rate on general Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. From this point onwards, we will refer to vehicles and objects interchangeably. 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. consists of three hierarchical steps, including efficient and accurate object 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. In this . This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. 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. Kalman filter coupled with the Hungarian algorithm for association, and 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. including near-accidents and accidents occurring at urban intersections are A popular . This framework was evaluated on diverse 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. 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. We determine this parameter by determining the angle () of a vehicle with respect to its own trajectories over a course of an interval of five frames. 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). Many people lose their lives in road accidents. The proposed framework is purposely designed with efficient algorithms in order to be applicable in real-time traffic monitoring systems. Otherwise, in case of no association, the state is predicted based on the linear velocity model. You can also use a downloaded video if not using a camera. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. 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. , to locate and classify the road-users at each video frame. Abandoned objects detection is one of the most crucial tasks in intelligent visual surveillance systems, especially in highway scenes [6, 15, 16].Various types of abandoned objects may be found on the road, such as vehicle parts left behind in a car accident, cargo dropped from a lorry, debris dropping from a slope, etc. 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. 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]. What is Accident Detection System? For certain scenarios where the backgrounds and objects are well defined, e.g., the roads and cars for highway traffic accidents detection, recent works [11, 19] are usually based on the frame-level annotated training videos (i.e., the temporal annotations of the anomalies in the training videos are available - supervised setting). 1 holds true. The inter-frame displacement of each detected object is estimated by a linear velocity model. 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. 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. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. 3. The model of computer-assisted analysis of lung ultrasound image is built which has shown great potential in pulmonary condition diagnosis and is also used as an alternative for diagnosis of COVID-19 in a patient. An accident Detection System is designed to detect accidents via video or CCTV footage. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. This section provides details about the three major steps in the proposed accident detection framework. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. The proposed framework consists of three hierarchical steps, including . Build a Vehicle Detection System using OpenCV and Python We are all set to build our vehicle detection system! All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. detection of road accidents is proposed. A tag already exists with the provided branch name. In this paper, a neoteric framework for detection of road accidents is proposed. In the UAV-based surveillance technology, video segments captured from . Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. Then the approaching angle of the a pair of road-users a and b is calculated as follows: where denotes the estimated approaching angle, ma and mb are the the general moving slopes of the road-users a and b with respect to the origin of the video frame, xta, yta, xtb, ytb represent the center coordinates of the road-users a and b at the current frame, xta and yta are the center coordinates of object a when first observed, xtb and ytb are the center coordinates of object b when first observed, respectively. The position dissimilarity is computed in a similar way: where the value of CPi,j is between 0 and 1, approaching more towards 1 when the object oi and detection oj are further. We then display this vector as trajectory for a given vehicle by extrapolating it. 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. One of the solutions, proposed by Singh et al. The first version of the You Only Look Once (YOLO) deep learning method was introduced in 2015 [21]. In this paper, a neoteric framework for detection of road accidents is proposed. To contribute to this project, knowledge of basic python scripting, Machine Learning, and Deep Learning will help. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. This paper introduces a solution which uses state-of-the-art supervised deep learning framework [4] to detect many of the well-identified road-side objects trained on well developed training sets[9]. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. different types of trajectory conflicts including vehicle-to-vehicle, 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. 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. This architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. They do not perform well in establishing standards for accident detection as they require specific forms of input and thereby cannot be implemented for a general scenario. 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. 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