computer vision based accident detection in traffic surveillance githubcomputer vision based accident detection in traffic surveillance github

Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. 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. 8 and a false alarm rate of 0.53 % calculated using Eq. pip install -r requirements.txt. road-traffic CCTV surveillance footage. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. A sample of the dataset is illustrated in Figure 3. If (L H), is determined from a pre-defined set of conditions on the value of . Kalman filter coupled with the Hungarian algorithm for association, and The trajectories are further analyzed to monitor the motion patterns of the detected road-users in terms of location, speed, and moving direction. The object trajectories The size dissimilarity is calculated based on the width and height information of the objects: where w and h denote the width and height of the object bounding box, respectively. A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. 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. 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 capitalizes on This paper introduces a solution which uses state-of-the-art supervised deep learning framework [4] to detect many of the well-identified road-side objects trained on well developed training sets[9]. The results are evaluated by calculating Detection and False Alarm Rates as metrics: The proposed framework achieved a Detection Rate of 93.10% and a False Alarm Rate of 6.89%. Many people lose their lives in road accidents. Dhananjai Chand2, Savyasachi Gupta 3, Goutham K 4, Assistant Professor, Department of Computer Science and Engineering, B.Tech., Department of Computer Science and Engineering, 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. 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. 1 holds true. The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. In this paper, a neoteric framework for detection of road accidents is proposed. 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. An accident Detection System is designed to detect accidents via video or CCTV footage. The performance is compared to other representative methods in table I. Sign up to our mailing list for occasional updates. 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. Many people lose their lives in road accidents. In this paper, a neoteric framework for detection of road accidents is proposed. Section III delineates the proposed framework of the paper. Similarly, Hui et al. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. In this paper, a neoteric framework for detection of road accidents is proposed. Therefore, computer vision techniques can be viable tools for automatic accident detection. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. The two averaged points p and q are transformed to the real-world coordinates using the inverse of the homography matrix H1, which is calculated during camera calibration [28] by selecting a number of points on the frame and their corresponding locations on the Google Maps [11]. 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Farhadi, You only look once: unified, real-time object detection, Proceedings of the IEEE conference on computer vision and pattern recognition, Anomalous driving detection for traffic surveillance video analysis, 2021 IEEE International Conference on Imaging Systems and Techniques (IST), H. Shi, H. Ghahremannezhadand, and C. Liu, A statistical modeling method for road recognition in traffic video analytics, 2020 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), A new foreground segmentation method for video analysis in different color spaces, 24th International Conference on Pattern Recognition, Z. Tang, G. Wang, H. Xiao, A. Zheng, and J. Hwang, Single-camera and inter-camera vehicle tracking and 3d speed estimation based on fusion of visual and semantic features, Proceedings of the IEEE conference on computer vision and pattern recognition workshops, A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition, L. Yue, M. Abdel-Aty, Y. Wu, O. Zheng, and J. Yuan, In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention, Computer Vision-based Accident Detection in Traffic Surveillance, Artificial Intelligence Enabled Traffic Monitoring System, Incident Detection on Junctions Using Image Processing, Automatic vehicle trajectory data reconstruction at scale, Real-time Pedestrian Surveillance with Top View Cumulative Grids, Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion 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. In the event of a collision, a circle encompasses the vehicles that collided is shown. Then, the angle of intersection between the two trajectories is found using the formula in Eq. Video processing was done using OpenCV4.0. As illustrated in fig. 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. We determine the speed of the vehicle in a series of steps. The probability of an accident is . 1 holds true. The object detection and object tracking modules are implemented asynchronously to speed up the calculations. to detect vehicular accidents used the feed of a CCTV surveillance camera by generating Spatio-Temporal Video Volumes (STVVs) and then extracting deep representations on denoising autoencoders in order to generate an anomaly score while simultaneously detecting moving objects, tracking the objects, and then finding the intersection of their tracks to finally determine the odds of an accident occurring. 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. This section describes our proposed framework given in Figure 2. The more different the bounding boxes of object oi and detection oj are in size, the more Ci,jS approaches one. Experimental evaluations demonstrate the feasibility of our method in real-time applications of traffic management. at: http://github.com/hadi-ghnd/AccidentDetection. The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. applications of traffic surveillance. 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. conditions such as broad daylight, low visibility, rain, hail, and snow using We used a desktop with a 3.4 GHz processor, 16 GB RAM, and an Nvidia GTX-745 GPU, to implement our proposed method. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. objects, and shape changes in the object tracking step. This is done for both the axes. The next criterion in the framework, C3, is to determine the speed of the vehicles. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. The dataset includes day-time and night-time videos of various challenging weather and illumination conditions. 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. Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. Using Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the video. 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. Add a 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. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. 4. A predefined number (B. ) 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. applied for object association to accommodate for occlusion, overlapping 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. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. In this paper, a new framework to detect vehicular collisions is proposed. In this paper, a new framework to detect vehicular collisions is proposed. The Hungarian algorithm [15] is used to associate the detected bounding boxes from frame to frame. 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]. at intersections for traffic surveillance applications. We estimate , the interval between the frames of the video, using the Frames Per Second (FPS) as given in Eq. 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 Overlap of bounding boxes of two vehicles plays a key role in this framework. 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 inter-frame displacement of each detected object is estimated by a linear velocity model. We then display this vector as trajectory for a given vehicle by extrapolating it. 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. 9. The proposed framework In this paper, a neoteric framework for detection of road accidents is proposed. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. A dataset of various traffic videos containing accident or near-accident scenarios is collected to test the performance of the proposed framework against real videos. Additionally, despite all the efforts in preventing hazardous driving behaviors, running the red light is still common. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. detected with a low false alarm rate and a high detection rate. Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. https://github.com/krishrustagi/Accident-Detection-System.git, To install all the packages required to run this python program Since here we are also interested in the category of the objects, we employ a state-of-the-art object detection method, namely YOLOv4 [2]. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. [4]. 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. We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. 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. 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. Multi Deep CNN Architecture, Is it Raining Outside? Section II succinctly debriefs related works and literature. Mask R-CNN is an instance segmentation algorithm that was introduced by He et al. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. A classifier is trained based on samples of normal traffic and traffic accident. 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