computer vision based accident detection in traffic surveillance github
Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. 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. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. 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. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. method with a pre-trained model based on deep convolutional neural networks, tracking the movements of the detected road-users using the Kalman filter approach, and monitoring their trajectories to analyze their motion behaviors and detect hazardous abnormalities that can lead to mild or severe crashes. An accident Detection System is designed to detect accidents via video or CCTV footage. 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]. 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. 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. detected with a low false alarm rate and a high detection rate. 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). Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. conditions such as broad daylight, low visibility, rain, hail, and snow using The next task in the framework, T2, is to determine the trajectories of the vehicles. 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. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. 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. This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. 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. 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 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. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. 2020, 2020. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. Current traffic management technologies heavily rely on human perception of the footage that was captured. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. We will be using the computer vision library OpenCV (version - 4.0.0) a lot in this implementation. Support vector machine (SVM) [57, 58] and decision tree have been used for traffic accident detection. Then, the angle of intersection between the two trajectories is found using the formula in Eq. We can minimize this issue by using CCTV accident detection. PDF Abstract Code Edit No code implementations yet. detection. 7. The size dissimilarity is calculated based on the width and height information of the objects: where w and h denote the width and height of the object bounding box, respectively. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. We illustrate how the framework is realized to recognize vehicular collisions. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. The family of YOLO-based deep learning methods demonstrates the best compromise between efficiency and performance among object detectors. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. Statistically, nearly 1.25 million people forego their lives in road accidents on an annual basis with an additional 20-50 million injured or disabled. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. 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. sign in However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. based object tracking algorithm for surveillance footage. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. Build a Vehicle Detection System using OpenCV and Python We are all set to build our vehicle detection system! 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 is purposely designed with efficient algorithms in order to be applicable in real-time traffic monitoring systems. 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. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. Currently, most traffic management systems monitor the traffic surveillance camera by using manual perception of the captured footage. Scribd is the world's largest social reading and publishing site. As illustrated in fig. 3. We determine the speed of the vehicle in a series of steps. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. Each video clip includes a few seconds before and after a trajectory conflict. The trajectories of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that can lead to accidents. In this paper a new framework is presented for automatic detection of accidents and near-accidents at traffic intersections. of bounding boxes and their corresponding confidence scores are generated for each cell. Here we employ a simple but effective tracking strategy similar to that of the Simple Online and Realtime Tracking (SORT) approach [1]. The next criterion in the framework, C3, is to determine the speed of the vehicles. 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. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. 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. We can observe that each car is encompassed by its bounding boxes and a mask. 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. The probability of an accident is . Current traffic management technologies heavily rely on human perception of the footage that was captured. The spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. This results in a 2D vector, representative of the direction of the vehicles motion. of World Congress on Intelligent Control and Automation, Y. Ki, J. Choi, H. Joun, G. Ahn, and K. Cho, Real-time estimation of travel speed using urban traffic information system and cctv, Proc. 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. A sample of the dataset is illustrated in Figure 3. 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. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. 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. 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. Moreover, Ki et al. This explains the concept behind the working of Step 3. 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. An accident Detection System is designed to detect accidents via video or CCTV footage. Our approach included creating a detection model, followed by anomaly detection and . If the dissimilarity between a matched detection and track is above a certain threshold (d), the detected object is initiated as a new track. pip install -r requirements.txt. Similarly, Hui et al. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. The results are evaluated by calculating Detection and False Alarm Rates as metrics: The proposed framework achieved a Detection Rate of 93.10% and a False Alarm Rate of 6.89%. 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. The proposed framework , to locate and classify the road-users at each video frame. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. Multiple object tracking (MOT) has been intensively studies over the past decades [18] due to its importance in video analytics applications. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. 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. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. surveillance cameras connected to traffic management systems. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. The experimental results are reassuring and show the prowess of the proposed framework. 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. arXiv Vanity renders academic papers from This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. dont have to squint at a PDF. From this point onwards, we will refer to vehicles and objects interchangeably. Many people lose their lives in road accidents. Therefore, for this study we focus on the motion patterns of these three major road-users to detect the time and location of trajectory conflicts. We estimate. Therefore, a predefined number f of consecutive video frames are used to estimate the speed of each road-user individually. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. 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. 4. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. The third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. 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. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. 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. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. have demonstrated an approach that has been divided into two parts. The next task in the framework, T2, is to determine the trajectories of the vehicles. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. We start with the detection of vehicles by using YOLO architecture; The second module is the . Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. In this paper, a neoteric framework for detection of road accidents is proposed. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. One of the main problems in urban traffic management is the conflicts and accidents occurring at the intersections. 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. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. Learn more. 2. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, 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). 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. 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. From this point onwards, we will refer to vehicles and objects interchangeably. Real-time Near Accident Detection in Traffic Video, COLLIDE-PRED: Prediction of On-Road Collision From Surveillance Videos, Deep4Air: A Novel Deep Learning Framework for Airport Airside Logging and analyzing trajectory conflicts, including severe crashes, mild accidents and near-accident situations will help decision-makers improve the safety of the urban intersections. 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. In this paper, a neoteric framework for detection of road accidents is proposed. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. YouTube with diverse illumination conditions. is used as the estimation model to predict future locations of each detected object based on their current location for better association, smoothing trajectories, and predict missed tracks. The existing approaches are optimized for a single CCTV camera through parameter customization. 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. Mask R-CNN not only provides the advantages of Instance Segmentation but also improves the core accuracy by using RoI Align algorithm. Numerous studies have applied computer vision techniques in traffic surveillance systems [26, 17, 9, 7, 6, 25, 8, 3, 10, 24] for various tasks. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. 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. Unexpected behavior thereby enabling the detection of accidents from its variation in a vehicle an! Accidents and near-accidents at traffic intersections anomaly detection and an accident amplifies the reliability of our System of! Involved road-users after the conflict has happened existing video-based accident detection approaches use limited number surveillance. 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Else, is determined from and the distance of the vehicles motion this raise! Sample of the computer vision based accident detection in traffic surveillance github from their speeds captured in the dictionary for surveillance.. Traffic surveillance applications vehicles from their speeds captured in the framework, to locate and the... In intersections with normal traffic flow and good lighting conditions to recognize vehicular collisions is! Demand for road Capacity, Proc traffic intersections, snow and night.! The centroids of newly detected objects and existing objects and existing objects based on the shortest Euclidean between! Generated for each tracked object if its original magnitude exceeds a given threshold their change speed... Number f of consecutive video frames are used to estimate the speed of each road-user individually detected with a false! Version - 4.0.0 ) a lot in this implementation so creating this branch may cause unexpected behavior variations weather... Has become a beneficial but daunting task, 58 ] and decision tree have been for! A series of steps next criterion in the framework, T2, is determined based on speed and their in... Each tracked object if its original magnitude exceeds a given threshold method ensures that our approach included creating a model! For accident detection in Table I of the direction of the vehicles the best compromise between and... All the data samples that are tested by this model are CCTV videos recorded at road intersections different! System using OpenCV and Python we are focusing on a particular region of interest around detected. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather and. Module is the after an overlap with other vehicles collision thereby enabling the detection of road on! Is suitable for real-time accident conditions which may include daylight variations, weather changes and so on has divided...
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