However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. 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. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. https://github.com/krishrustagi/Accident-Detection-System.git, To install all the packages required to run this python program of IEEE International Conference on Computer Vision (ICCV), W. Hu, X. Xiao, D. Xie, T. Tan, and S. Maybank, Traffic accident prediction using 3-d model-based vehicle tracking, in IEEE Transactions on Vehicular Technology, Z. Hui, X. Yaohua, M. Lu, and F. Jiansheng, Vision-based real-time traffic accident detection, Proc. Computer Vision-based Accident Detection in Traffic Surveillance - Free download as PDF File (.pdf), Text File (.txt) or read online for free. Selecting the region of interest will start violation detection system. traffic video data show the feasibility of the proposed method in real-time Typically, anomaly detection methods learn the normal behavior via training. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. 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. This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Note: This project requires a camera. Open navigation menu. The trajectories of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that can lead to accidents. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. The framework is built of five modules. 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. method to achieve a high Detection Rate and a low False Alarm Rate on general Sun, Robust road region extraction in video under various illumination and weather conditions, 2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS), A new adaptive bidirectional region-of-interest detection method for intelligent traffic video analysis, A real time accident detection framework for traffic video analysis, Machine Learning and Data Mining in Pattern Recognition, MLDM, Automatic road detection in traffic videos, 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), A new online approach for moving cast shadow suppression in traffic videos, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), E. 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Redmon, S. Divvala, R. Girshick, and A. 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 For everything else, email us at [emailprotected]. Road accidents are a significant problem for the whole world. Therefore, a predefined number f of consecutive video frames are used to estimate the speed of each road-user individually. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. The second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. 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. 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. Surveillance Cameras, 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. 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. 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. Furthermore, Figure 5 contains samples of other types of incidents detected by our framework, including near-accidents, vehicle-to-bicycle (V2B), and vehicle-to-pedestrian (V2P) conflicts. 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. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. dont have to squint at a PDF. the proposed dataset. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. 2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Deep spatio-temporal representation for detection of road accidents using stacked autoencoder, 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. This is done for both the axes. The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. Leaving abandoned objects on the road for long periods is dangerous, so . are analyzed in terms of velocity, angle, and distance in order to detect of the proposed framework is evaluated using video sequences collected from 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. In this paper, a neoteric framework for detection of road accidents is proposed. The moving direction and speed of road-user pairs that are close to each other are examined based on their trajectories in order to detect anomalies that can cause them to crash. arXiv as responsive web pages so you A sample of the dataset is illustrated in Figure 3. The proposed framework 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. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. based object tracking algorithm for surveillance footage. 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]. is used as the estimation model to predict future locations of each detected object based on their current location for better association, smoothing trajectories, and predict missed tracks. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. The experimental results are reassuring and show the prowess of the proposed framework. Work fast with our official CLI. 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. 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. 3. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. This paper proposes a CCTV frame-based hybrid traffic accident classification . Therefore, computer vision techniques can be viable tools for automatic accident detection. The layout of this paper is as follows. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. These steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [2]. In this section, details about the heuristics used to detect conflicts between a pair of road-users are presented. The surveillance videos at 30 frames per second (FPS) are considered. 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. 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. Section IV contains the analysis of our experimental results. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. In this paper, a neoteric framework for detection of road accidents is proposed. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. Similarly, Hui et al. The more different the bounding boxes of object oi and detection oj are in size, the more Ci,jS approaches one. Current traffic management technologies heavily rely on human perception of the footage that was captured. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. We then normalize this vector by using scalar division of the obtained vector by its magnitude. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. The next criterion in the framework, C3, is to determine the speed of the vehicles. Currently, most traffic management systems monitor the traffic surveillance camera by using manual perception of the captured footage. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. Let x, y be the coordinates of the centroid of a given vehicle and let , be the width and height of the bounding box of a vehicle respectively. Each video clip includes a few seconds before and after a trajectory conflict. Therefore, for this study we focus on the motion patterns of these three major road-users to detect the time and location of trajectory conflicts. The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. The first version of the You Only Look Once (YOLO) deep learning method was introduced in 2015 [21]. There was a problem preparing your codespace, please try again. Fig. The robust tracking method accounts for challenging situations, such as occlusion, overlapping objects, and shape changes in tracking the objects of interest and recording their trajectories. All the experiments conducted in relation to this framework validate the potency and efficiency of the proposition and thereby authenticates the fact that the framework can render timely, valuable information to the concerned authorities. The third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. 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. Due to the lack of a publicly available benchmark for traffic accidents at urban intersections, we collected 29 short videos from YouTube that contain 24 vehicle-to-vehicle (V2V), 2 vehicle-to-bicycle (V2B), and 3 vehicle-to-pedestrian (V2P) trajectory conflict cases. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. 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. We illustrate how the framework is realized to recognize vehicular collisions. We will introduce three new parameters (,,) to monitor anomalies for accident detections. 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. Traffic closed-circuit television (CCTV) devices can be used to detect and track objects on roads by designing and applying artificial intelligence and deep learning models. Add a of IEEE International Conference on Computer Vision (ICCV), W. Hu, X. Xiao, D. Xie, T. Tan, and S. Maybank, Traffic accident prediction using 3-d model-based vehicle tracking, in IEEE Transactions on Vehicular Technology, Z. Hui, X. Yaohua, M. Lu, and F. Jiansheng, Vision-based real-time traffic accident detection, Proc. 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. So make sure you have a connected camera to your device. From this point onwards, we will refer to vehicles and objects interchangeably. 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. The spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure 1. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. Currently, I am experimenting with cutting-edge technology to unleash cleaner energy sources to power the world.<br>I have a total of 8 . Computer vision-based accident detection through video surveillance has This explains the concept behind the working of Step 3. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. Scribd is the world's largest social reading and publishing site. 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. detection based on the state-of-the-art YOLOv4 method, object tracking based on Timely detection of such trajectory conflicts is necessary for devising countermeasures to mitigate their potential harms. Additionally, the Kalman filter approach [13]. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: The average processing speed is 35 frames per second (fps) which is feasible for real-time applications. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. 9. The next criterion in the framework, C3, is to determine the speed of the vehicles. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure. 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