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. of bounding boxes and their corresponding confidence scores are generated for each cell. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. 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. 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]. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. Current traffic management technologies heavily rely on human perception of the footage that was captured. Similarly, Hui et al. 9. The trajectories of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that can lead to accidents. 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. The proposed framework consists of three hierarchical steps, including . 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 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. 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. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. Automatic detection of traffic incidents not only saves a great deal of unnecessary manual labor, but the spontaneous feedback also helps the paramedics and emergency ambulances to dispatch in a timely fashion. at intersections for traffic surveillance applications. Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. 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. These steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [2]. This section describes our proposed framework given in Figure 2. In the event of a collision, a circle encompasses the vehicles that collided is shown. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. 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. In section II, the major steps of the proposed accident detection framework, including object detection (section II-A), object tracking (section II-B), and accident detection (section II-C) are discussed. Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. Figure 4 shows sample accident detection results by our framework given videos containing vehicle-to-vehicle (V2V) side-impact collisions. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Mask R-CNN for accurate object detection followed by an efficient centroid 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. 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. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. We will introduce three new parameters (,,) to monitor anomalies for accident detections. Traffic accidents include different scenarios, such as rear-end, side-impact, single-car, vehicle rollovers, or head-on collisions, each of which contain specific characteristics and motion patterns. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. Computer Vision-based Accident Detection in Traffic Surveillance Abstract: Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. The second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. 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. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. pip install -r requirements.txt. The framework is built of five modules. These object pairs can potentially engage in a conflict and they are therefore, chosen for further analysis. detection based on the state-of-the-art YOLOv4 method, object tracking based on 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. arXiv as responsive web pages so you PDF Abstract Code Edit No code implementations yet. Want to hear about new tools we're making? Support vector machine (SVM) [57, 58] and decision tree have been used for traffic accident detection. become a beneficial but daunting task. 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. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. 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. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. This is done for both the axes. A new cost function is We determine the speed of the vehicle in a series of steps. 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. Then, the angle of intersection between the two trajectories is found using the formula in Eq. arXiv Vanity renders academic papers from We then normalize this vector by using scalar division of the obtained vector by its magnitude. The family of YOLO-based deep learning methods demonstrates the best compromise between efficiency and performance among object detectors. From this point onwards, we will refer to vehicles and objects interchangeably. The surveillance videos at 30 frames per second (FPS) are considered. 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 This paper conducted an extensive literature review on the applications of . 5. 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. This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. Therefore, In computer vision, anomaly detection is a sub-field of behavior understanding from surveillance scenes. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. the development of general-purpose vehicular accident detection algorithms in This is achieved with the help of RoI Align by overcoming the location misalignment issue suffered by RoI Pooling which attempts to fit the blocks of the input feature map. Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. In this . Anomalies are typically aberrations of scene entities (people, vehicles, environment) and their interactions from normal behavior. Authors: Authors: Babak Rahimi Ardabili, Armin Danesh Pazho, Ghazal Alinezhad Noghre, Christopher Neff, Sai Datta Bhaskararayuni, Arun Ravindran, Shannon Reid, Hamed Tabkhi Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computer Vision and . 3. 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. Google Scholar [30]. 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. A popular . This explains the concept behind the working of Step 3. 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). 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. Therefore, a predefined number f of consecutive video frames are used to estimate the speed of each road-user individually. Road accidents are a significant problem for the whole world. Let's first import the required libraries and the modules. 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. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. 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. Timely detection of such trajectory conflicts is necessary for devising countermeasures to mitigate their potential harms. The proposed 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. 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. Experimental evaluations demonstrate the feasibility of our method in real-time applications of traffic management. 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. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure. The surveillance videos at 30 frames per second (FPS) are considered. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. Use Git or checkout with SVN using the web URL. 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. Before running the program, you need to run the accident-classification.ipynb file which will create the model_weights.h5 file. 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. 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. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. Using Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the video. An accident Detection System is designed to detect accidents via video or CCTV footage. In this paper, a new framework to detect vehicular collisions is proposed. One of the main problems in urban traffic management is the conflicts and accidents occurring at the intersections. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. method to achieve a high Detection Rate and a low False Alarm Rate on general Here we employ a simple but effective tracking strategy similar to that of the Simple Online and Realtime Tracking (SORT) approach [1]. The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. 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. 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. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. 2. of the proposed framework is evaluated using video sequences collected from Section IV contains the analysis of our experimental results. 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. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. This paper introduces a solution which uses state-of-the-art supervised deep learning framework. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. The next task in the framework, T2, is to determine the trajectories of the vehicles. 2020, 2020. The framework integrates three major modules, including object detection based on YOLOv4 method, a tracking method based on Kalman filter and Hungarian algorithm with a new cost function, and an accident detection module to analyze the extracted trajectories for anomaly detection. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. Computer vision-based accident detection through video surveillance has Considering two adjacent video frames t and t+1, we will have two sets of objects detected at each frame as follows: Every object oi in set Ot is paired with an object oj in set Ot+1 that can minimize the cost function C(oi,oj). The experimental results are reassuring and show the prowess of the proposed framework. 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. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. 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]. The performance is compared to other representative methods in table I. We then display this vector as trajectory for a given vehicle by extrapolating it. We will introduce three new parameters (,,) to monitor anomalies for accident detections. I used to be involved in major radioactive and explosive operations on daily basis!<br>Now that I get your attention, click the "See More" button:<br><br><br>Since I was a kid, I have always been fascinated by technology and how it transformed the world. Nowadays many urban intersections are equipped with The trajectory conflicts are detected and reported in real-time with only 2 instances of false alarms which is an acceptable rate considering the imperfections in the detection and tracking results. 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. 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. including near-accidents and accidents occurring at urban intersections are conditions such as broad daylight, low visibility, rain, hail, and snow using 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. Fig. Work fast with our official CLI. surveillance cameras connected to traffic management systems. We start with the detection of vehicles by using YOLO architecture; The second module is the . The trajectories are further analyzed to monitor the motion patterns of the detected road-users in terms of location, speed, and moving direction. The dataset is publicly available As illustrated in fig. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: The object detection and object tracking modules are implemented asynchronously to speed up the calculations. Many people lose their lives in road accidents. Other dangerous behaviors, such as sudden lane changing and unpredictable pedestrian/cyclist movements at the intersection, may also arise due to the nature of traffic control systems or intersection geometry. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. The average processing speed is 35 frames per second (fps) which is feasible for real-time applications. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 8 and a false alarm rate of 0.53 % calculated using Eq. Section III provides details about the collected dataset and experimental results and the paper is concluded in section section IV. have demonstrated an approach that has been divided into two parts. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. The layout of the rest of the paper is as follows. In this paper, a neoteric framework for detection of road accidents is proposed. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. 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 Hungarian algorithm [15] is used to associate the detected bounding boxes from frame to frame. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. We will be using the computer vision library OpenCV (version - 4.0.0) a lot in this implementation. traffic video data show the feasibility of the proposed method in real-time If the dissimilarity between a matched detection and track is above a certain threshold (d), the detected object is initiated as a new track. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. Computer Vision-based Accident Detection in Traffic Surveillance - Free download as PDF File (.pdf), Text File (.txt) or read online for free. 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. 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