Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. The more different the bounding boxes of object oi and detection oj are in size, the more Ci,jS approaches one. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. 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]. Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. traffic video data show the feasibility of the proposed method in real-time The next criterion in the framework, C3, is to determine the speed of the vehicles. This is the key principle for detecting an accident. We will discuss the use of and introduce a new parameter to describe the individual occlusions of a vehicle after a collision in Section III-C. Experimental evaluations demonstrate the feasibility of our method in real-time applications of traffic management. We determine the speed of the vehicle in a series of steps. 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 Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. 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. 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. task. 3. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. Add a Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. 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. We then normalize this vector by using scalar division of the obtained vector by its magnitude. Are you sure you want to create this branch? The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. Additionally, the Kalman filter approach [13]. Current traffic management technologies heavily rely on human perception of the footage that was captured. As a result, numerous approaches have been proposed and developed to solve this problem. detection based on the state-of-the-art YOLOv4 method, object tracking based on 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. 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 efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. 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. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. Scribd is the world's largest social reading and publishing site. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. after an overlap with other vehicles. , " A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition," Journal of advanced transportation, vol. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. Here we employ a simple but effective tracking strategy similar to that of the Simple Online and Realtime Tracking (SORT) approach [1]. 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. 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 next criterion in the framework, C3, is to determine the speed of the vehicles. Work fast with our official CLI. Additionally, it performs unsatisfactorily because it relies only on trajectory intersections and anomalies in the traffic flow pattern, which indicates that it wont perform well in erratic traffic patterns and non-linear trajectories. Currently, I am experimenting with cutting-edge technology to unleash cleaner energy sources to power the world.<br>I have a total of 8 . Recently, traffic accident detection is becoming one of the interesting fields due to its tremendous application potential in Intelligent . Computer Vision-based Accident Detection in Traffic Surveillance - Free download as PDF File (.pdf), Text File (.txt) or read online for free. However, extracting useful information from the detected objects and determining the occurrence of traffic accidents are usually difficult. The first version of the You Only Look Once (YOLO) deep learning method was introduced in 2015 [21]. 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. In the event of a collision, a circle encompasses the vehicles that collided is shown. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. 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]. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure 1. 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. 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. 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 case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. YouTube with diverse illumination conditions. become a beneficial but daunting task. We can observe that each car is encompassed by its bounding boxes and a mask. This results in a 2D vector, representative of the direction of the vehicles motion. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. of the proposed framework is evaluated using video sequences collected from Accident Detection, Mask R-CNN, Vehicular Collision, Centroid based Object Tracking, Earnest Paul Ijjina1 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 experimental results are reassuring and show the prowess of the proposed framework. The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Object detection for dummies part 3: r-cnn family, Faster r-cnn: towards real-time object detection with region proposal networks, in IEEE Transactions on Pattern Analysis and Machine Intelligence, Road traffic injuries and deathsa global problem, Deep spatio-temporal representation for detection of road accidents using stacked autoencoder, https://lilianweng.github.io/lil-log/assets/images/rcnn-family-summary.png, https://www.asirt.org/safe-travel/road-safety-facts/, https://www.cdc.gov/features/globalroadsafety/index.html. Please The third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. For everything else, email us at [emailprotected]. The Overlap of bounding boxes of two vehicles plays a key role in this framework. The proposed framework achieved a detection rate of 71 % calculated using Eq. Multi Deep CNN Architecture, Is it Raining Outside? The existing approaches are optimized for a single CCTV camera through parameter customization. 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. 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. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. The GitHub link contains the source code for this deep learning final year project => Covid-19 Detection in Lungs. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. Computer Vision-based Accident Detection in Traffic Surveillance Abstract: Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. One of the solutions, proposed by Singh et al. 1 holds true. Otherwise, we discard it. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. The spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. 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 repository majorly explores how CCTV can detect these accidents with the help of Deep Learning. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. 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. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid 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. 9. In particular, trajectory conflicts, All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. 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. 2020, 2020. The magenta line protruding from a vehicle depicts its trajectory along the direction. Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. 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 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. 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). 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. These steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [2]. The proposed accident detection algorithm includes the following key tasks: Vehicle Detection Vehicle Tracking and Feature Extraction Accident Detection The proposed framework realizes its intended purpose via the following stages: Iii-a Vehicle Detection This phase of the framework detects vehicles in the video. We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. Build a Vehicle Detection System using OpenCV and Python We are all set to build our vehicle detection system! The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. We used a desktop with a 3.4 GHz processor, 16 GB RAM, and an Nvidia GTX-745 GPU, to implement our proposed method. The proposed framework achieved a detection rate of 71 % calculated using Eq. 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 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 Kalman filter approach [ 13 ] results in a 2D vector representative., and direction interesting road-users by applying the state-of-the-art YOLOv4 [ 2 ] the in! Proposed framework capitalizes on mask R-CNN for accurate object computer vision based accident detection in traffic surveillance github followed by an centroid. 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