road-traffic CCTV surveillance footage. The next criterion in the framework, C3, is to determine the speed of the vehicles. 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. 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. 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 provides a robust method to achieve a high Detection Rate and a low False Alarm Rate on general road-traffic CCTV surveillance footage. Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. 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. Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. The family of YOLO-based deep learning methods demonstrates the best compromise between efficiency and performance among object detectors. Edit social preview. of bounding boxes and their corresponding confidence scores are generated for each cell. Many people lose their lives in road accidents. The magenta line protruding from a vehicle depicts its trajectory along the direction. In this paper, a neoteric framework for detection of road accidents is proposed. Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. 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. The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. The result of this phase is an output dictionary containing all the class IDs, detection scores, bounding boxes, and the generated masks for a given video frame. Google Scholar [30]. Kalman filter coupled with the Hungarian algorithm for association, and A new cost function is after an overlap with other vehicles. 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. 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. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. We then display this vector as trajectory for a given vehicle by extrapolating it. If you find a rendering bug, file an issue on GitHub. If nothing happens, download GitHub Desktop and try again. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. 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. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. 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. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). Accident Detection, Mask R-CNN, Vehicular Collision, Centroid based Object Tracking, Earnest Paul Ijjina1 8 and a false alarm rate of 0.53 % calculated using Eq. 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. detected with a low false alarm rate and a high detection rate. 3. 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 experimental results are reassuring and show the prowess of the proposed framework. The recent motion patterns of each pair of close objects are examined in terms of speed and moving direction. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. Build a Vehicle Detection System using OpenCV and Python We are all set to build our vehicle detection system! To use this project Python Version > 3.6 is recommended. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. One of the solutions, proposed by Singh et al. 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. for smoothing the trajectories and predicting missed objects. To contribute to this project, knowledge of basic python scripting, Machine Learning, and Deep Learning will help. 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. Anomalies are typically aberrations of scene entities (people, vehicles, environment) and their interactions from normal behavior. [4]. A popular . Before running the program, you need to run the accident-classification.ipynb file which will create the model_weights.h5 file. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. 1 holds true. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. One of the main problems in urban traffic management is the conflicts and accidents occurring at the intersections. 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. Another factor to account for in the detection of accidents and near-accidents is the angle of collision. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. 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. Experimental results using real 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. A vision-based real time traffic accident detection method to extract foreground and background from video shots using the Gaussian Mixture Model to detect vehicles; afterwards, the detected vehicles are tracked based on the mean shift algorithm. You can also use a downloaded video if not using a camera. at intersections for traffic surveillance applications. 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. 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. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. We can use an alarm system that can call the nearest police station in case of an accident and also alert them of the severity of the accident. 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 [15]. 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. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. The dataset is publicly available The proposed framework is purposely designed with efficient algorithms in order to be applicable in real-time traffic monitoring systems. 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 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. There was a problem preparing your codespace, please try again. Additionally, despite all the efforts in preventing hazardous driving behaviors, running the red light is still common. Our framework is able to report the occurrence of trajectory conflicts along with the types of the road-users involved immediately. 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. 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