Accident Detection, Mask R-CNN, Vehicular Collision, Centroid based Object Tracking, Earnest Paul Ijjina1 Section IV contains the analysis of our experimental results. We then normalize this vector by using scalar division of the obtained vector by its magnitude. In particular, trajectory conflicts, Computer vision -based accident detection through video surveillance has become a beneficial but daunting task. Section III provides details about the collected dataset and experimental results and the paper is concluded in section section IV. Therefore, computer vision techniques can be viable tools for automatic accident detection. 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. 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. 4. 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. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. for smoothing the trajectories and predicting missed objects. Open navigation menu. We can minimize this issue by using CCTV accident detection. 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). A tag already exists with the provided branch name. 2020, 2020. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. If (L H), is determined from a pre-defined set of conditions on the value of . 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. Papers With Code is a free resource with all data licensed under. We then normalize this vector by using scalar division of the obtained vector by its magnitude. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. dont have to squint at a PDF. We start with the detection of vehicles by using YOLO architecture; The second module is the . 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 next task in the framework, T2, is to determine the trajectories of the vehicles. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. 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. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. 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 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. detect anomalies such as traffic accidents in real time. Mask R-CNN for accurate object detection followed by an efficient centroid The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. Video processing was done using OpenCV4.0. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. Mask R-CNN is an instance segmentation algorithm that was introduced by He et al. 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. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure. 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. 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). Timely detection of such trajectory conflicts is necessary for devising countermeasures to mitigate their potential harms. In this paper, a neoteric framework for detection of road accidents is proposed. to use Codespaces. based object tracking algorithm for surveillance footage. The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. 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. Kalman filter coupled with the Hungarian algorithm for association, and Use Git or checkout with SVN using the web URL. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. 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. 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 proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. This results in a 2D vector, representative of the direction of the vehicles motion. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. Want to hear about new tools we're making? 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. Leaving abandoned objects on the road for long periods is dangerous, so . 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. The proposed framework achieved a detection rate of 71 % calculated using Eq. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. Selecting the region of interest will start violation detection system. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. The first version of the You Only Look Once (YOLO) deep learning method was introduced in 2015 [21]. 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. are analyzed in terms of velocity, angle, and distance in order to detect computer vision techniques can be viable tools for automatic accident 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. 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 Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. 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. Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. In this paper, a neoteric framework for detection of road accidents is proposed. 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 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. 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. 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. The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. of bounding boxes and their corresponding confidence scores are generated for each cell. surveillance cameras connected to traffic management systems. The proposed framework 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). We then determine the magnitude of the vector. As a result, numerous approaches have been proposed and developed to solve this problem. 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. The bounding boxes do overlap but the scenario does not necessarily lead to an accident that our is! Accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection through surveillance... 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computer vision based accident detection in traffic surveillance github