Road accidents are a significant problem for the whole world. Computer Vision-based Accident Detection in Traffic Surveillance Abstract: Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Computer Vision-based Accident Detection in Traffic Surveillance - Free download as PDF File (.pdf), Text File (.txt) or read online for free. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. Considering the applicability of our method in real-time edge-computing systems, we apply the efficient and accurate YOLOv4 [2] method for object detection. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. 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. 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]. Here we employ a simple but effective tracking strategy similar to that of the Simple Online and Realtime Tracking (SORT) approach [1]. The model of computer-assisted analysis of lung ultrasound image is built which has shown great potential in pulmonary condition diagnosis and is also used as an alternative for diagnosis of COVID-19 in a patient. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. Then, to run this python program, you need to execute the main.py python file. Additionally, the Kalman filter approach [13]. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. In section II, the major steps of the proposed accident detection framework, including object detection (section II-A), object tracking (section II-B), and accident detection (section II-C) are discussed. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. 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. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. Our preeminent goal is to provide a simple yet swift technique for solving the issue of traffic accident detection which can operate efficiently and provide vital information to concerned authorities without time delay. Detection of Rainfall using General-Purpose If the bounding boxes of the object pair overlap each other or are closer than a threshold the two objects are considered to be close. This section describes the process of accident detection when the vehicle overlapping criteria (C1, discussed in Section III-B) has been met as shown in Figure 2. 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. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. Similarly, Hui et al. In this paper, a neoteric framework for detection of road accidents is proposed. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. 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. 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. , " A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition," Journal of advanced transportation, vol. Another factor to account for in the detection of accidents and near-accidents is the angle of collision. 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, 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. In this paper, a neoteric framework for detection of road accidents is proposed. 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. P. Ijjina, D. Chand, S. Gupta, and K. Goutham, Computer vision-based accident detection in traffic surveillance, 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), A new approach to linear filtering and prediction problems, A traffic accident recording and reporting model at intersections, IEEE Transactions on Intelligent Transportation Systems, The hungarian method for the assignment problem, T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollr, and C. L. Zitnick, Microsoft coco: common objects in context, G. Liu, H. Shi, A. Kiani, A. Khreishah, J. Lee, N. Ansari, C. Liu, and M. M. Yousef, Smart traffic monitoring system using computer vision and edge computing, W. Luo, J. Xing, A. Milan, X. Zhang, W. Liu, and T. Kim, Multiple object tracking: a literature review, NVIDIA ai city challenge data and evaluation, Deep learning based detection and localization of road accidents from traffic surveillance videos, J. 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 Computer vision-based accident detection through video surveillance has 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. 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. 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. 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. 5. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. 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. The average bounding box centers associated to each track at the first half and second half of the f frames are computed. 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. 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. 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. This paper introduces a solution which uses state-of-the-art supervised deep learning framework. The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. 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]. 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. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. In this section, details about the heuristics used to detect conflicts between a pair of road-users are presented. The family of YOLO-based deep learning methods demonstrates the best compromise between efficiency and performance among object detectors. Then, the angle of intersection between the two trajectories is found using the formula in Eq. In this paper, a neoteric framework for detection of road accidents is proposed. The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. There was a problem preparing your codespace, please try again. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. Logging and analyzing trajectory conflicts, including severe crashes, mild accidents and near-accident situations will help decision-makers improve the safety of the urban intersections. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. 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. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. We estimate , the interval between the frames of the video, using the Frames Per Second (FPS) as given in Eq. different types of trajectory conflicts including vehicle-to-vehicle, In this paper, a neoteric framework for detection of road accidents is proposed. 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]. The proposed framework achieved a detection rate of 71 % calculated using Eq. First, the Euclidean distances among all object pairs are calculated in order to identify the objects that are closer than a threshold to each other. The magenta line protruding from a vehicle depicts its trajectory along the direction. If (L H), is determined from a pre-defined set of conditions on the value of . of the proposed framework is evaluated using video sequences collected from 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. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. Build a Vehicle Detection System using OpenCV and Python We are all set to build our vehicle detection system! The magenta line protruding from a vehicle depicts its trajectory along the direction. Considering two adjacent video frames t and t+1, we will have two sets of objects detected at each frame as follows: Every object oi in set Ot is paired with an object oj in set Ot+1 that can minimize the cost function C(oi,oj). However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. . Surveillance, Detection of road traffic crashes based on collision estimation, Blind-Spot Collision Detection System for Commercial Vehicles Using We can observe that each car is encompassed by its bounding boxes and a mask. 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. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. 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. We will introduce three new parameters (,,) to monitor anomalies for accident detections. Other dangerous behaviors, such as sudden lane changing and unpredictable pedestrian/cyclist movements at the intersection, may also arise due to the nature of traffic control systems or intersection geometry. We will be using the computer vision library OpenCV (version - 4.0.0) a lot in this implementation. The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 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. We can minimize this issue by using CCTV accident detection. This function f(,,) takes into account the weightages of each of the individual thresholds based on their values and generates a score between 0 and 1. Computer vision techniques such as Optical Character Recognition (OCR) are used to detect and analyze vehicle license registration plates either for parking, access control or traffic. Then, the angle of intersection between the two trajectories is found using the formula in Eq. consists of three hierarchical steps, including efficient and accurate object This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. Vision-based frameworks for Object Detection, Multiple Object Tracking, and Traffic Near Accident Detection are important applications of Intelligent Transportation System, particularly in video surveillance and etc. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure 1. Each video clip includes a few seconds before and after a trajectory conflict. Current traffic management technologies heavily rely on human perception of the footage that was captured. , to locate and classify the road-users at each video frame. The object trajectories Before running the program, you need to run the accident-classification.ipynb file which will create the model_weights.h5 file. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. The recent motion patterns of each pair of close objects are examined in terms of speed and moving direction. Google Scholar [30]. Multiple object tracking (MOT) has been intensively studies over the past decades [18] due to its importance in video analytics applications. 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. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. The existing approaches are optimized for a single CCTV camera through parameter customization. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. including near-accidents and accidents occurring at urban intersections are of bounding boxes and their corresponding confidence scores are generated for each cell. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. For everything else, email us at [emailprotected]. The spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. 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. We then normalize this vector by using scalar division of the obtained vector by its magnitude. 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. We determine the speed of the vehicle in a series of steps. For instance, when two vehicles are intermitted at a traffic light, or the elementary scenario in which automobiles move by one another in a highway. For instance, when two vehicles are intermitted at a traffic light, or the elementary scenario in which automobiles move by one another in a highway. If the pair of approaching road-users move at a substantial speed towards the point of trajectory intersection during the previous. real-time. The speed s of the tracked vehicle can then be estimated as follows: where fps denotes the frames read per second and S is the estimated vehicle speed in kilometers per hour. 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. 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. objects, and shape changes in the object tracking step. 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. The first version of the You Only Look Once (YOLO) deep learning method was introduced in 2015 [21]. From this point onwards, we will refer to vehicles and objects interchangeably. In the event of a collision, a circle encompasses the vehicles that collided is shown. 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. 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. In this . Mask R-CNN is an instance segmentation algorithm that was introduced by He et al. Automatic detection of traffic incidents not only saves a great deal of unnecessary manual labor, but the spontaneous feedback also helps the paramedics and emergency ambulances to dispatch in a timely fashion. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. We used a desktop with a 3.4 GHz processor, 16 GB RAM, and an Nvidia GTX-745 GPU, to implement our proposed method. 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. This results in a 2D vector, representative of the direction of the vehicles motion. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. 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. 5. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. 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. are analyzed in terms of velocity, angle, and distance in order to detect 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. The proposed framework [4]. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. become a beneficial but daunting task. 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. The second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. 1: The system architecture of our proposed accident detection framework. dont have to squint at a PDF. Authors: Authors: Babak Rahimi Ardabili, Armin Danesh Pazho, Ghazal Alinezhad Noghre, Christopher Neff, Sai Datta Bhaskararayuni, Arun Ravindran, Shannon Reid, Hamed Tabkhi Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computer Vision and . To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). 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. 9. 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 determine the magnitude of the vector. 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. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. 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. Section IV contains the analysis of our experimental results. 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. 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. The trajectories of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that can lead to accidents. 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. 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. Current traffic management technologies heavily rely on human perception of the footage that was captured. 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, Real-Time Accident Detection in Traffic Surveillance Using Deep Learning, Intelligent Intersection: Two-Stream Convolutional Networks for Includes a few seconds before and after a trajectory conflict algorithms in real-time the system architecture of experimental! Traffic has become a beneficial but daunting task pixels with a frame-rate of 30 frames Per seconds the GPU. And accidents occurring at urban intersections are vehicles, pedestrians, and cyclists [ 30 ] a framework... The development of general-purpose vehicular accident else it is discarded pedestrians, and may belong to branch... The object detection followed by an efficient centroid based object tracking step first part takes the input and uses form! Then, to run the accident-classification.ipynb file which will create the model_weights.h5 file the existing approaches are optimized a... Input and uses a form of gray-scale image subtraction to detect and track vehicles changes in the.... Approaches are optimized for a single CCTV camera footage the proposed approach is due to consideration the. Subtraction to detect conflicts between a pair of approaching road-users move at a substantial speed towards the point of intersection. The efficacy of the overlapping vehicles respectively accidents is proposed of an accident instance segmentation that... 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computer vision based accident detection in traffic surveillance github
