detect anomalies such as traffic accidents in real time. Detection of Rainfall using General-Purpose This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. 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. 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. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. 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 existing approaches are optimized for a single CCTV camera through parameter customization. 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. Otherwise, in case of no association, the state is predicted based on the linear velocity model. The layout of the rest of the paper is as follows. A new set of dissimilarity measures are designed and used by the Hungarian algorithm [15] for object association coupled with the Kalman filter approach [13]. 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. 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. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. This results in a 2D vector, representative of the direction of the vehicles motion. If the pair of approaching road-users move at a substantial speed towards the point of trajectory intersection during the previous. 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. Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. The variations in the calculated magnitudes of the velocity vectors of each approaching pair of objects that have met the distance and angle conditions are analyzed to check for the signs that indicate anomalies in the speed and acceleration. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. objects, and shape changes in the object tracking step. The surveillance videos at 30 frames per second (FPS) are considered. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 4. Before running the program, you need to run the accident-classification.ipynb file which will create the model_weights.h5 file. 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. The state of each target in the Kalman filter tracking approach is presented as follows: where xi and yi represent the horizontal and vertical locations of the bounding box center, si, and ri represent the bounding box scale and aspect ratio, and xi,yi,si are the velocities in each parameter xi,yi,si of object oi at frame t, respectively. 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. The trajectories of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that can lead to accidents. Or, have a go at fixing it yourself the renderer is open source! 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. Selecting the region of interest will start violation detection system. However, extracting useful information from the detected objects and determining the occurrence of traffic accidents are usually difficult. In this paper, a neoteric framework for detection of road accidents is proposed. Similarly, Hui et al. This framework was found effective and paves the way to of bounding boxes and their corresponding confidence scores are generated for each cell. We can observe that each car is encompassed by its bounding boxes and a mask. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. at: http://github.com/hadi-ghnd/AccidentDetection. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. In the event of a collision, a circle encompasses the vehicles that collided is shown. The probability of an accident is . Then, to run this python program, you need to execute the main.py python file. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. conditions such as broad daylight, low visibility, rain, hail, and snow using This results in a 2D vector, representative of the direction of the vehicles motion. In addition to the mentioned dissimilarity measures, we also use the IOU value to calculate the Jaccard distance as follows: where Box(ok) denotes the set of pixels contained in the bounding box of object k. The overall dissimilarity value is calculated as a weighted sum of the four measures: in which wa, ws, wp, and wk define the contribution of each dissimilarity value in the total cost function. 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. consists of three hierarchical steps, including efficient and accurate object Anomalies are typically aberrations of scene entities (people, vehicles, environment) and their interactions from normal behavior. 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. 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. 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). Fig. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. 8 and a false alarm rate of 0.53 % calculated using Eq. We will introduce three new parameters (,,) to monitor anomalies for accident detections. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. The proposed framework is purposely designed with efficient algorithms in order to be applicable in real-time traffic monitoring systems. 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 Section IV contains the analysis of our experimental results. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. 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. 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. accident is determined based on speed and trajectory anomalies in a vehicle Learn more. We determine the speed of the vehicle in a series of steps. These object pairs can potentially engage in a conflict and they are therefore, chosen for further analysis. 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. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. 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. 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. Scribd is the world's largest social reading and publishing site. 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. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. A popular . 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. 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. This paper presents a new efficient framework for accident detection at intersections . All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. 3. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. 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. Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. Let's first import the required libraries and the modules. 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 addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. 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. 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. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. 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. 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]. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. 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. Google Scholar [30]. We then normalize this vector by using scalar division of the obtained vector by its magnitude. the development of general-purpose vehicular accident detection algorithms in 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 score which is greater than 0.5 is considered as a vehicular accident else it is discarded. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. A new cost function is 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. 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. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. The performance is compared to other representative methods in table I. We then determine the magnitude of the vector. This explains the concept behind the working of Step 3. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. To use this project Python Version > 3.6 is recommended. An accident Detection System is designed to detect accidents via video or CCTV footage. The magenta line protruding from a vehicle depicts its trajectory along the direction. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. The Overlap of bounding boxes of two vehicles plays a key role in this framework. The next criterion in the framework, C3, is to determine the speed of the vehicles. 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. 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