Scribd is the world's largest social reading and publishing site. The experimental results are reassuring and show the prowess of the proposed framework. In this section, details about the heuristics used to detect conflicts between a pair of road-users are presented. The layout of the rest of the paper is as follows. Computer Vision-based Accident Detection in Traffic Surveillance - Free download as PDF File (.pdf), Text File (.txt) or read online for free. 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. Abstract: In Intelligent Transportation System, real-time systems that monitor and analyze road users become increasingly critical as we march toward the smart city era. 2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Deep spatio-temporal representation for detection of road accidents using stacked autoencoder, This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. The intersection over union (IOU) of the ground truth and the predicted boxes is multiplied by the probability of each object to compute the confidence scores. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. Section III provides details about the collected dataset and experimental results and the paper is concluded in section section IV. traffic monitoring systems. Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. The probability of an accident is . We estimate , the interval between the frames of the video, using the Frames Per Second (FPS) as given in Eq. This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. detection. Accident Detection, Mask R-CNN, Vehicular Collision, Centroid based Object Tracking, Earnest Paul Ijjina1 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. 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. 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 spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. 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. 3. 8 and a false alarm rate of 0.53 % calculated using Eq. 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. 1 holds true. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. In computer vision, anomaly detection is a sub-field of behavior understanding from surveillance scenes. This paper introduces a solution which uses state-of-the-art supervised deep learning framework. In this paper, a new framework to detect vehicular collisions is proposed. The Hungarian algorithm [15] is used to associate the detected bounding boxes from frame to frame. 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. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. 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. The inter-frame displacement of each detected object is estimated by a linear velocity model. 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. As a result, numerous approaches have been proposed and developed to solve this problem. Kalman filter coupled with the Hungarian algorithm for association, and of bounding boxes and their corresponding confidence scores are generated for each cell. In this paper, a neoteric framework for 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. These object pairs can potentially engage in a conflict and they are therefore, chosen for further analysis. We then display this vector as trajectory for a given vehicle by extrapolating it. A predefined number (B. ) In this paper, a neoteric framework for detection of road accidents is proposed. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. accident is determined based on speed and trajectory anomalies in a vehicle The probability of an As a result, numerous approaches have been proposed and developed to solve this problem. Vision-based frameworks for Object Detection, Multiple Object Tracking, and Traffic Near Accident Detection are important applications of Intelligent Transportation System, particularly in video surveillance and etc. This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. What is Accident Detection System? 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. 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. 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. We can minimize this issue by using CCTV accident detection. In this paper, a neoteric framework for detection of road accidents is proposed. The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. 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. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. 7. The two averaged points p and q are transformed to the real-world coordinates using the inverse of the homography matrix H1, which is calculated during camera calibration [28] by selecting a number of points on the frame and their corresponding locations on the Google Maps [11]. Similarly, Hui et al. 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. traffic video data show the feasibility of the proposed method in real-time We determine the speed of the vehicle in a series of steps. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. In this . 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. sign in One of the main problems in urban traffic management is the conflicts and accidents occurring at the intersections. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. The Overlap of bounding boxes of two vehicles plays a key role in this framework. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. The proposed framework provides a robust method to achieve a high Detection Rate and a low False Alarm Rate on general road-traffic CCTV surveillance footage. We then normalize this vector by using scalar division of the obtained vector by its magnitude. From this point onwards, we will refer to vehicles and objects interchangeably. 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. 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 [6]. 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. The distance in kilometers can then be calculated by applying the haversine formula [4] as follows: where p and q are the latitudes, p and q are the longitudes of the first and second averaged points p and q, respectively, h is the haversine of the central angle between the two points, r6371 kilometers is the radius of earth, and dh(p,q) is the distance between the points p and q in real-world plane in kilometers. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. 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. The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. Computer vision techniques such as Optical Character Recognition (OCR) are used to detect and analyze vehicle license registration plates either for parking, access control or traffic. after an overlap with other vehicles. 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. 9. This is done for both the axes. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. 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]. Another factor to account for in the detection of accidents and near-accidents is the angle of collision. This paper presents a new efficient framework for accident detection 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. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. This repository majorly explores how CCTV can detect these accidents with the help of Deep Learning. 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. You can also use a downloaded video if not using a camera. 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. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. 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. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. The average bounding box centers associated to each track at the first half and second half of the f frames are computed. 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 overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. the proposed dataset. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. 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. 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. Experimental results using real Consider a, b to be the bounding boxes of two vehicles A and B. 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. In this paper, a neoteric framework for detection of road accidents is proposed. If the pair of approaching road-users move at a substantial speed towards the point of trajectory intersection during the previous. 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. We then determine the magnitude of the vector, , as shown in Eq. 2020, 2020. 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. 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. Typically, anomaly detection methods learn the normal behavior via training. We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. Want to hear about new tools we're making? A sample of the dataset is illustrated in Figure 3. In the event of a collision, a circle encompasses the vehicles that collided is shown. In the area of computer vision, deep neural networks (DNNs) have been used to analyse visual events by learning the spatio-temporal features from training samples. computer vision techniques can be viable tools for automatic accident The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. Section II succinctly debriefs related works and literature. 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. Build a Vehicle Detection System using OpenCV and Python We are all set to build our vehicle detection system! For everything else, email us at [emailprotected]. 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. We can observe that each car is encompassed by its bounding boxes and a mask. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: Currently, most traffic management systems monitor the traffic surveillance camera by using manual perception of the captured footage. 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. Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. 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. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. The proposed framework is purposely designed with efficient algorithms in order to be applicable in real-time traffic monitoring systems. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. This is done for both the axes. 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. 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). A tag already exists with the provided branch name. Sign up to our mailing list for occasional updates. 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. 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]. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of 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 explains the concept behind the working of Step 3. 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. arXiv Vanity renders academic papers from 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. Fig. A classifier is trained based on samples of normal traffic and traffic accident. The proposed framework capitalizes on All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. Activity recognition in unmanned aerial vehicle (UAV) surveillance is addressed in various computer vision applications such as image retrieval, pose estimation, object detection, object detection in videos, object detection in still images, object detection in video frames, face recognition, and video action recognition. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. different types of trajectory conflicts including vehicle-to-vehicle, The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. Selecting the region of interest will start violation detection system. The framework is built of five modules. 9. Leaving abandoned objects on the road for long periods is dangerous, so . The next task in the framework, T2, is to determine the trajectories of the vehicles. PDF Abstract Code Edit No code implementations yet. 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. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. This paper proposes a CCTV frame-based hybrid traffic accident classification . 5. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. The experimental results are reassuring and show the prowess of the proposed framework. Computer vision -based accident detection through video surveillance has become a beneficial but daunting task. 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). We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. If (L H), is determined from a pre-defined set of conditions on the value of . Hence, this paper proposes a pragmatic solution for addressing aforementioned problem by suggesting a solution to detect Vehicular Collisions almost spontaneously which is vital for the local paramedics and traffic departments to alleviate the situation in time. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. at intersections for traffic surveillance applications. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. The object detection and object tracking modules are implemented asynchronously to speed up the calculations. The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. 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. 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). In the event of a collision, a circle encompasses the vehicles that collided is shown. This framework was evaluated on. 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. the development of general-purpose vehicular accident detection algorithms in 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. Drivers caught in a dilemma zone may decide to accelerate at the time of phase change from green to yellow, which in turn may induce rear-end and angle crashes. We can minimize this issue by using CCTV accident detection. One of the solutions, proposed by Singh et al. 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. To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). 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. The dataset is publicly available 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. This framework was evaluated on diverse The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure. arXiv as responsive web pages so you Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. We start with the detection of vehicles by using YOLO architecture; The second module is the . Experimental evaluations demonstrate the feasibility of our method in real-time applications of traffic management. [4]. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. 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. 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.

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