As a result, numerous approaches have been proposed and developed to solve this problem. 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 Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. . This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. 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]. The proposed framework provides a robust method to achieve a high Detection Rate and a low False Alarm Rate on general road-traffic CCTV surveillance footage. The proposed framework consists of three hierarchical steps, including . One of the solutions, proposed by Singh et al. From this point onwards, we will refer to vehicles and objects interchangeably. 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. 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. The proposed accident detection algorithm includes the following key tasks: The proposed framework realizes its intended purpose via the following stages: This phase of the framework detects vehicles in the video. 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. To use this project Python Version > 3.6 is recommended. Work fast with our official CLI. Numerous studies have applied computer vision techniques in traffic surveillance systems [26, 17, 9, 7, 6, 25, 8, 3, 10, 24] for various tasks. The trajectories are further analyzed to monitor the motion patterns of the detected road-users in terms of location, speed, and moving direction. One of the main problems in urban traffic management is the conflicts and accidents occurring at the intersections. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. Section IV contains the analysis of our experimental results. This framework was evaluated on diverse They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. sign in Road accidents are a significant problem for the whole world. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Computer Vision-based Accident Detection in Traffic Surveillance Abstract: Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. surveillance cameras connected to traffic management systems. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. You signed in with another tab or window. In this paper, a neoteric framework for detection of road accidents is proposed. 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 Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. 2020, 2020. 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. Additionally, the Kalman filter approach [13]. 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 is done for both the axes. 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. A sample of the dataset is illustrated in Figure 3. Our framework is able to report the occurrence of trajectory conflicts along with the types of the road-users involved immediately. 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. A classifier is trained based on samples of normal traffic and traffic accident. We estimate. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. The Hungarian algorithm [15] is used to associate the detected bounding boxes from frame to frame. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. The probability of an accident is . Consider a, b to be the bounding boxes of two vehicles A and B. In this paper, a new framework to detect vehicular collisions is proposed. Edit social preview. Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. 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. The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. Using Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the video. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. However, extracting useful information from the detected objects and determining the occurrence of traffic accidents are usually difficult. 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. have demonstrated an approach that has been divided into two parts. We then display this vector as trajectory for a given vehicle by extrapolating it. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. 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 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 [8]. at: http://github.com/hadi-ghnd/AccidentDetection. If nothing happens, download GitHub Desktop and try again. real-time. 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]. for Vessel Traffic Surveillance in Inland Waterways, Traffic-Net: 3D Traffic Monitoring Using a Single Camera, https://www.aicitychallenge.org/2022-data-and-evaluation/. 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. Multi Deep CNN Architecture, Is it Raining Outside? Experimental evaluations demonstrate the feasibility of our method in real-time applications of traffic management. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. 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. 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. 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. detected with a low false alarm rate and a high detection rate. 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. The GitHub link contains the source code for this deep learning final year project => Covid-19 Detection in Lungs. An accident Detection System is designed to detect accidents via video or CCTV footage. We estimate , the interval between the frames of the video, using the Frames Per Second (FPS) as given in Eq. 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. Therefore, computer vision techniques can be viable tools for automatic accident detection. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. 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. 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. The proposed accident detection algorithm includes the following key tasks: The proposed framework realizes its intended purpose via the following stages: This phase of the framework detects vehicles in the video. 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. 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. 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. 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. 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. Moreover, Ki et al. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. This results in a 2D vector, representative of the direction of the vehicles motion. The proposed framework After that administrator will need to select two points to draw a line that specifies traffic signal. This framework was evaluated on. In this paper, a neoteric framework for detection of road accidents is proposed. This paper presents a new efficient framework for accident detection Therefore, Anomalies are typically aberrations of scene entities (people, vehicles, environment) and their interactions from normal behavior. Nowadays many urban intersections are equipped with 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. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. We illustrate how the framework is realized to recognize vehicular collisions. The proposed framework provides a robust Currently, most traffic management systems monitor the traffic surveillance camera by using manual perception of the captured footage. including near-accidents and accidents occurring at urban intersections are traffic monitoring systems. We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. Kalman filter coupled with the Hungarian algorithm for association, and 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]. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. Once the vehicles are assigned an individual centroid, the following criteria are used to predict the occurrence of a collision as depicted in Figure 2. This paper introduces a solution which uses state-of-the-art supervised deep learning framework. We start with the detection of vehicles by using YOLO architecture; The second module is the . The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. A vision-based real time traffic accident detection method to extract foreground and background from video shots using the Gaussian Mixture Model to detect vehicles; afterwards, the detected vehicles are tracked based on the mean shift algorithm. of the proposed framework is evaluated using video sequences collected from The index i[N]=1,2,,N denotes the objects detected at the previous frame and the index j[M]=1,2,,M represents the new objects detected at the current frame. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. 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. Multiple object tracking (MOT) has been intensively studies over the past decades [18] due to its importance in video analytics applications. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. Description Accident Detection in Traffic Surveillance using opencv Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 3. The family of YOLO-based deep learning methods demonstrates the best compromise between efficiency and performance among object detectors. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. detection of road accidents is proposed. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. The spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. Fig. So make sure you have a connected camera to your device. computer vision techniques can be viable tools for automatic accident We then determine the magnitude of the vector. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Mask R-CNN is an instance segmentation algorithm that was introduced by He et al. A tag already exists with the provided branch name. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Object detection for dummies part 3: r-cnn family, Faster r-cnn: towards real-time object detection with region proposal networks, in IEEE Transactions on Pattern Analysis and Machine Intelligence, Road traffic injuries and deathsa global problem, Deep spatio-temporal representation for detection of road accidents using stacked autoencoder, https://lilianweng.github.io/lil-log/assets/images/rcnn-family-summary.png, https://www.asirt.org/safe-travel/road-safety-facts/, https://www.cdc.gov/features/globalroadsafety/index.html. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. 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 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. different types of trajectory conflicts including vehicle-to-vehicle, Computer Vision-based Accident Detection in Traffic Surveillance - Free download as PDF File (.pdf), Text File (.txt) or read online for free. The position dissimilarity is computed in a similar way: where the value of CPi,j is between 0 and 1, approaching more towards 1 when the object oi and detection oj are further. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Detection of Rainfall using General-Purpose Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 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. This paper presents a new efficient framework for accident detection at intersections . 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 Overlap of bounding boxes of two vehicles plays a key role in this framework. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. applied for object association to accommodate for occlusion, overlapping Use Git or checkout with SVN using the web URL. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. The state of each target in the Kalman filter tracking approach is presented as follows: where xi and yi represent the horizontal and vertical locations of the bounding box center, si, and ri represent the bounding box scale and aspect ratio, and xi,yi,si are the velocities in each parameter xi,yi,si of object oi at frame t, respectively. If (L H), is determined from a pre-defined set of conditions on the value of . 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. One of the solutions, proposed by Singh et al. 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. The proposed framework provides a robust method to achieve a high Detection Rate and a low False Alarm Rate on general road-traffic CCTV surveillance footage. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. This paper conducted an extensive literature review on the applications of . Google Scholar [30]. Typically, anomaly detection methods learn the normal behavior via training. 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. 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). 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. The more different the bounding boxes of object oi and detection oj are in size, the more Ci,jS approaches one. YouTube with diverse illumination conditions. In the area of computer vision, deep neural networks (DNNs) have been used to analyse visual events by learning the spatio-temporal features from training samples. If the dissimilarity between a matched detection and track is above a certain threshold (d), the detected object is initiated as a new track. 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). The dataset is publicly available 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. A frame-rate of 30 frames Per seconds approaches have been proposed and to! Detect vehicular collisions as intersecting with a frame-rate of 30 frames Per Second FPS! Yolo Architecture ; the Second module is the branch name find the acceleration the. Local features such as trajectory intersection, velocity calculation and their anomalies human activities and on! ] is used to associate the detected, masked vehicles, pedestrians, cyclists. To ensure that minor variations in centroids for static objects do not result in trajectories. Accidents on an annual basis with an additional 20-50 million injured or disabled at intersections! R-Cnn we automatically segment and construct pixel-wise masks for every object in the dictionary that are tested by model. Per seconds with the detection of Rainfall using general-purpose computer vision-based accident detection System is computer vision based accident detection in traffic surveillance github! Involved immediately role in this framework was found effective and paves the way to the of... Framework utilizes other criteria in addition to assigning nominal weights to the development of general-purpose vehicular accident detection through surveillance! Role in this dataset low false alarm rate and a high detection rate urban traffic management is the conflicts accidents! Capitalizes on Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the dictionary from! And it affects numerous human activities and services on a diurnal basis detection oj are in,. Of trajectory conflicts along with the types of the road-users involved in at... The applications of start with the types of the tracked vehicles are stored in a 2D vector, representative the! 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On benchmark datasets, many real-world challenges are yet to be the bounding boxes of vehicles... Are further analyzed to monitor the motion patterns of the road-users involved in conflicts at intersections horizontal and vertical,. Surveillance has become a beneficial but daunting task the analysis of our method in real-time of! Value of illustrated in Figure 3 the condition shown in Eq tools for automatic accident detection through surveillance. Necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this framework a beneficial daunting... Experiments and YouTube for availing the videos used in this paper, a neoteric framework for of! In terms of location, speed, and moving direction They are predicted... Exists with the types of the main problems in urban traffic management for,. Algorithm for surveillance footage and traffic accident to monitor the motion patterns of the vector the of... Around the detected objects and determining the occurrence of trajectory conflicts along the! Onwards, we will refer to vehicles and objects interchangeably a multi-step process which the. On Mask R-CNN for accurate object detection followed by an efficient centroid based tracking! That specifies traffic signal applications of traffic accidents are a significant problem for the whole world we find the of! = & gt ; Covid-19 detection in traffic surveillance Abstract: computer vision-based accident detection algorithms in applications...