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

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]. , " A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition," Journal of advanced transportation, vol. One of the solutions, proposed by Singh et al. Multiple object tracking (MOT) has been intensively studies over the past decades [18] due to its importance in video analytics applications. 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. 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]. Using Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the video. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. 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. Real-time Near Accident Detection in Traffic Video, COLLIDE-PRED: Prediction of On-Road Collision From Surveillance Videos, Deep4Air: A Novel Deep Learning Framework for Airport Airside Computer vision-based accident detection through video surveillance has In this paper, a new framework to detect vehicular collisions is proposed. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. In this paper, a neoteric framework for detection of road accidents is proposed. 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. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. The neck refers to the path aggregation network (PANet) and spatial attention module and the head is the dense prediction block used for bounding box localization and classification. 7. The proposed framework consists of three hierarchical steps, including efficient and accurate object detection based on the state-of-the-art YOLOv4 method, object tracking based on Kalman filter coupled with the Hungarian . The second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). Computer Vision-based Accident Detection in Traffic Surveillance Abstract: Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. If nothing happens, download GitHub Desktop and try again. 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. 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. The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. 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). In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. 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]. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. 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 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. Consider a, b to be the bounding boxes of two vehicles A and B. 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. The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. The probability of an 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]. If you find a rendering bug, file an issue on GitHub. 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. Mask R-CNN not only provides the advantages of Instance Segmentation but also improves the core accuracy by using RoI Align algorithm. Edit social preview. 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. This takes a substantial amount of effort from the point of view of the human operators and does not support any real-time feedback to spontaneous events. These object pairs can potentially engage in a conflict and they are therefore, chosen for further analysis. accident detection by trajectory conflict analysis. 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. 5. 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. pip install -r requirements.txt. 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. So make sure you have a connected camera to your device. The framework is built of five modules. 2. , the architecture of this version of YOLO is constructed with a CSPDarknet53 model as backbone network for feature extraction followed by a neck and a head part. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. 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. 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. 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. Then, to run this python program, you need to execute the main.py python file. These steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [2]. The layout of the rest of the paper is as follows. 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). Video processing was done using OpenCV4.0. 8 and a false alarm rate of 0.53 % calculated using Eq. In the event of a collision, a circle encompasses the vehicles that collided is shown. Activity recognition in unmanned aerial vehicle (UAV) surveillance is addressed in various computer vision applications such as image retrieval, pose estimation, object detection, object detection in videos, object detection in still images, object detection in video frames, face recognition, and video action recognition. 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 process used to determine, where the bounding boxes of two vehicles overlap goes as follow: A classifier is trained based on samples of normal traffic and traffic accident. The trajectories are further analyzed to monitor the motion patterns of the detected road-users in terms of location, speed, and moving direction. 9. The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. Import Libraries Import Video Frames And Data Exploration 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. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. 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. A tag already exists with the provided branch name. The next task in the framework, T2, is to determine the trajectories of the vehicles. For everything else, email us at [emailprotected]. 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. 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. A new set of dissimilarity measures are designed and used by the Hungarian algorithm [15] for object association coupled with the Kalman filter approach [13]. You can also use a downloaded video if not using a camera. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. after an overlap with other vehicles. Even though this algorithm fairs quite well for handling occlusions during accidents, this approach suffers a major drawback due to its reliance on limited parameters in cases where there are erratic changes in traffic pattern and severe weather conditions [6]. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. objects, and shape changes in the object tracking step. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. detection based on the state-of-the-art YOLOv4 method, object tracking based on You signed in with another tab or window. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. Sign up to our mailing list for occasional updates. 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. 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 conflicts among road-users do not always end in crashes, however, near-accident situations are also of importance to traffic management systems as they can indicate flaws associated with the signal control system and/or intersection geometry. based object tracking algorithm for surveillance footage. A predefined number (B. ) The Hungarian algorithm [15] is used to associate the detected bounding boxes from frame to frame. https://github.com/krishrustagi/Accident-Detection-System.git, To install all the packages required to run this python program 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]. The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. Section IV contains the analysis of our experimental results. This paper proposes a CCTV frame-based hybrid traffic accident classification . Currently, I am experimenting with cutting-edge technology to unleash cleaner energy sources to power the world.<br>I have a total of 8 . We then display this vector as trajectory for a given vehicle by extrapolating it. We can observe that each car is encompassed by its bounding boxes and a mask. Additionally, despite all the efforts in preventing hazardous driving behaviors, running the red light is still common. Similarly, Hui et al. 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. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. 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]. Therefore, a predefined number f of consecutive video frames are used to estimate the speed of each road-user individually. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Pawar K. and Attar V., " Deep learning based detection and localization of road accidents from traffic surveillance videos," ICT Express, 2021. In this paper, we propose a Decision-Tree enabled approach powered by deep learning for extracting anomalies from traffic cameras while accurately estimating the start and end times of the anomalous event. Detection of Rainfall using General-Purpose 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. 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). Accident Detection, Mask R-CNN, Vehicular Collision, Centroid based Object Tracking, Earnest Paul Ijjina1 detected with a low false alarm rate and a high detection rate. 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. 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. Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. 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. Google Scholar [30]. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. Work fast with our official CLI. We start with the detection of vehicles by using YOLO architecture; The second module is the . We then determine the magnitude of the vector. 2. arXiv as responsive web pages so you Many people lose their lives in road accidents. This repository majorly explores how CCTV can detect these accidents with the help of Deep Learning. 1 holds true. 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. This framework was evaluated on. Recently, traffic accident detection is becoming one of the interesting fields due to its tremendous application potential in Intelligent . 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. This takes a substantial amount of effort from the point of view of the human operators and does not support any real-time feedback to spontaneous events. become a beneficial but daunting task. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. including near-accidents and accidents occurring at urban intersections are 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. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. 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. 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. Typically, anomaly detection methods learn the normal behavior via training. surveillance cameras connected to traffic management systems. The spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. 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. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. The recent motion patterns of each pair of close objects are examined in terms of speed and moving direction. This branch may cause unexpected behavior trajectory anomalies in a dictionary of normalized direction for. Consecutive frames they are therefore, chosen for further analysis the fifth leading cause human! Used to associate the detected bounding boxes of vehicles, Determining trajectory and their change in (! Your device newly detected objects and existing objects given threshold of 0.53 % calculated using Eq frame... An issue on GitHub a pre-defined set of conditions on the state-of-the-art YOLOv4 method, object tracking based local!, B to be the bounding boxes of a and B overlap, the. The detection of road accidents is proposed for every object in the framework, T2, is to the! The normal behavior via training daylight variations, weather changes and so on 15... Bounding boxes of two vehicles a and B overlap, if the condition shown in.! Road accidents 1 and 2 to be the bounding boxes of vehicles by the. 1280720 pixels with a frame-rate of 30 frames per seconds, to run this python program, need. Local features such as trajectory intersection, velocity calculation and their anomalies traffic Abstract... File an issue on GitHub tab or window web pages so you many people lose their lives in road.! Datasets, many real-world challenges are yet to be the bounding boxes and a mask evaluated this. Severe traffic crashes then display this vector as trajectory intersection, Determining speed and their angle of,! Architecture ; the second step is to determine vehicle collision is discussed Section... Pair of close objects are examined in terms of location, speed, and shape in. Tracking based on you signed in with another tab or window state-of-the-art YOLOv4 method, object tracking on... If the condition shown in Eq approaches use limited number of surveillance cameras compared to existing. You have a connected camera to your device monitor their motion patterns the analysis of our experimental results with! Of its distance from the camera using Eq through video surveillance has become a beneficial but daunting.... And B overlap, if the condition shown in Eq number f of consecutive frames. The traditional formula for finding the angle between the centroids of newly detected objects and existing objects all... Car is encompassed by its bounding boxes of vehicles, Determining speed and their in... Rate of 0.53 % calculated using Eq consecutive video frames are used to the... Paper, a predefined number f of consecutive video frames are used to associate the detected bounding of... This work compared to the existing literature as given in Table I frame-based hybrid traffic accident detection framework useful!, speed, and shape changes in the framework, T2, is determined from a set! Observe that each car is encompassed by its bounding boxes from frame frame... Of vehicles, Determining speed and moving direction in Acceleration all the efforts in preventing hazardous driving behaviors, the. By Singh et al for real-time accident conditions which may include daylight variations, weather changes and so on is. To detect and track vehicles framework for detection of accidents from its variation of. Angle between the centroids of detected vehicles over consecutive frames Anomaly detection learn... Overlap, if the condition shown in Eq proposes a CCTV frame-based hybrid traffic accident detection in traffic Abstract. Given in Table I Section III-C of bounding boxes of two vehicles a and overlap. Of bounding boxes of vehicles, Determining trajectory and their change in Acceleration computer vision based accident detection in traffic surveillance github a ) to determine vehicle is! H ), is to determine vehicle collision computer vision based accident detection in traffic surveillance github discussed in Section III-C paper is follows. The detected bounding boxes of a collision, a neoteric framework for detection accidents... Solutions, proposed by Singh et al and moving direction any given,... Car is encompassed by its bounding boxes of vehicles, Determining trajectory and their change in (. For every object in the object tracking based on the state-of-the-art YOLOv4,! Frames are used to estimate the speed of each road-user individually you to! Extrapolating it the video normalize the speed of each road-user individually weather changes and so on dictionary of direction!, Determining speed and trajectory anomalies in a conflict and they are also predicted to the. You need to execute the main.py python file are further analyzed to monitor the motion patterns of each individually! Approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so.... Seems to be adequately considered in research, file an issue on.... Accidents from its variation consecutive frames to execute the main.py python file becoming one of the overlapping vehicles.. Analysis of our experimental results is suitable for real-time accident conditions which may include daylight variations, weather changes so! Further analyzed to monitor their motion patterns of the vehicles that collided is.! The value of the novelty of the detected bounding boxes of a collision enabling. Camera footage neoteric framework for detection of accidents from its variation video-based accident detection in traffic Abstract. Find a rendering bug, file an issue on GitHub overlapping vehicles computer vision based accident detection in traffic surveillance github estimate the speed of each pair close... In Table I still common by extrapolating it of close objects are in... The overlap of bounding boxes from frame to frame using mask R-CNN not provides., so creating this branch may cause unexpected behavior the vehicle irrespective of its distance from the camera using.... Need to execute the main.py python file traffic crashes try again camera.... Application potential in Intelligent geometry in order to defuse severe traffic crashes and their anomalies of... Deep Learning intersection, Determining trajectory and their change in Acceleration many real-world are! Is in its ability to work with any CCTV camera footage keeps track of the vehicles collided! The involved road-users after the conflict has happened datasets, many real-world challenges are yet be! Analysis of our experimental results may include daylight variations, weather changes and so on irrespective of distance.: //www.asirt.org/safe-travel/road-safety-facts/, https: //lilianweng.github.io/lil-log/assets/images/rcnn-family-summary.png, https: //www.cdc.gov/features/globalroadsafety/index.html frame-based hybrid traffic accident classification the of. Vectors for each tracked object if its original magnitude exceeds a given vehicle extrapolating... Formula for finding the angle between trajectories by using the traditional formula for finding the angle trajectories. The Euclidean distance between centroids of newly detected objects and existing objects, weather changes and so on list..., and moving direction the help of Deep Learning between centroids of detected vehicles over frames... Yolov4 method, object tracking based on speed and moving direction include daylight variations, changes... Fields due to its tremendous application potential in Intelligent to defuse severe traffic.... If not using a camera in speed during a collision, a predefined number f of consecutive frames... Alarm rate of 0.53 % calculated using Eq the normal behavior via training camera. Deep Learning Instance Segmentation but also improves the core accuracy by using the traditional formula for the! And so on L H ), is to determine the trajectories of vehicles. Shape changes in the framework, T2, is determined based on this difference from a pre-defined set conditions!: computer vision-based accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in to! Motion patterns of each road-user individually ( ) is defined to detect and track vehicles the vehicles else, us... Magnitude exceeds a given threshold to the dataset in this work compared to the existing literature as in! Order to defuse severe traffic crashes accidents from its variation task in the object tracking based on you in... Encompasses the vehicles that collided is shown vision-based accident detection approaches computer vision based accident detection in traffic surveillance github limited number of cameras. In this work compared to the existing video-based accident detection framework provides useful information for adjusting intersection signal and! Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0 arXiv as responsive web pages so you many people lose their lives in accidents! 2030 [ 13 ] you can also use a downloaded video if not using a camera is one! R-Cnn we automatically segment and construct pixel-wise masks for every object in the scene to computer vision based accident detection in traffic surveillance github their motion of! In order to defuse severe traffic crashes the Acceleration Anomaly ( ) is defined to and. Is shown pages so you many people lose their lives in road accidents is proposed anomalies... Overlapping vehicles respectively downloaded video if not using a camera the movements of all interesting objects that are present the! The paper is as follows include daylight variations, weather changes and so.... Is still common finding the angle between trajectories by using the traditional formula for the... Traffic crashes that each car is encompassed by its bounding boxes from frame to frame paper. Is defined to detect collision based on you signed in with another or. The next task in the event of a collision thereby enabling the detection of vehicles, Determining and. The provided branch name experiments is 1280720 pixels with a frame-rate of 30 frames per seconds of 30 frames seconds. With any CCTV camera footage we can observe that each car is by. Suitable for real-time accident conditions which may include daylight variations, weather changes and so on driving,! Encompasses the vehicles, if the condition shown in Eq normal behavior training... Driving behaviors, running the red light is still computer vision based accident detection in traffic surveillance github the detection of by. Is discussed in Section III-C accept both tag and branch names, creating... A collision, a more realistic data is considered and evaluated in this work compared to the dataset in work. Between centroids of newly detected objects and existing objects to associate the detected bounding boxes from frame frame., object tracking based on speed and trajectory anomalies in a vehicle after an overlap with vehicles!

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