Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. A sample of the dataset is illustrated in Figure 3. This is achieved with the help of RoI Align by overcoming the location misalignment issue suffered by RoI Pooling which attempts to fit the blocks of the input feature map. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. 5. The existing approaches are optimized for a single CCTV camera through parameter customization. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. 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). The next criterion in the framework, C3, is to determine the speed of the vehicles. This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. If you find a rendering bug, file an issue on GitHub. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. 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. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. The experimental results are reassuring and show the prowess of the proposed framework. 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. Papers With Code is a free resource with all data licensed under. 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. The performance is compared to other representative methods in table I. 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. have demonstrated an approach that has been divided into two parts. This explains the concept behind the working of Step 3. The layout of the rest of the paper is as follows. The framework is built of five modules. In this section, details about the heuristics used to detect conflicts between a pair of road-users are presented. Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. Our framework is able to report the occurrence of trajectory conflicts along with the types of the road-users involved immediately. traffic monitoring systems. 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. applied for object association to accommodate for occlusion, overlapping A tag already exists with the provided branch name. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. 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. This results in a 2D vector, representative of the direction of the vehicles motion. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. 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. 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. 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. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. 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 use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. 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 overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. 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. Abstract: In Intelligent Transportation System, real-time systems that monitor and analyze road users become increasingly critical as we march toward the smart city era. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. 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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 At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. Currently, most traffic management systems monitor the traffic surveillance camera by using manual perception of the captured footage. Experimental evaluations demonstrate the feasibility of our method in real-time applications of traffic management. 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. Kalman filter coupled with the Hungarian algorithm for association, and We can minimize this issue by using CCTV accident detection. the development of general-purpose vehicular accident detection algorithms in method to achieve a high Detection Rate and a low False Alarm Rate on general 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. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. In the event of a collision, a circle encompasses the vehicles that collided is shown. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. 9. A classifier is trained based on samples of normal traffic and traffic accident. 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. Then, to run this python program, you need to execute the main.py python file. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. If (L H), is determined from a pre-defined set of conditions on the value of . Typically, anomaly detection methods learn the normal behavior via training. 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. Different heuristic cues are considered in the motion analysis in order to detect anomalies that can lead to traffic accidents. The dataset includes day-time and night-time videos of various challenging weather and illumination conditions. In the UAV-based surveillance technology, video segments captured from . Before running the program, you need to run the accident-classification.ipynb file which will create the model_weights.h5 file. We will be using the computer vision library OpenCV (version - 4.0.0) a lot in this implementation. Drivers caught in a dilemma zone may decide to accelerate at the time of phase change from green to yellow, which in turn may induce rear-end and angle crashes. Please 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. In this paper, a neoteric framework for detection of road accidents is proposed. The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. Otherwise, we discard it. 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. Section III delineates the proposed framework of the paper. Timely detection of such trajectory conflicts is necessary for devising countermeasures to mitigate their potential harms. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. Accident Detection, Mask R-CNN, Vehicular Collision, Centroid based Object Tracking, Earnest Paul Ijjina1 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). In this . Consider a, b to be the bounding boxes of two vehicles A and B. including near-accidents and accidents occurring at urban intersections are The surveillance videos at 30 frames per second (FPS) are considered. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. . , " A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition," Journal of advanced transportation, vol. traffic video data show the feasibility of the proposed method in real-time Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage to achieve a high Detection Rate and a low False Alarm Rate on general road-traffic CCTV surveillance footage. arXiv as responsive web pages so you The Overlap of bounding boxes of two vehicles plays a key role in this framework. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. 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 Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. As illustrated in fig. 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. Moreover, Ki et al. Considering the applicability of our method in real-time edge-computing systems, we apply the efficient and accurate YOLOv4 [2] method for object detection. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. Nowadays many urban intersections are equipped with 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. We then normalize this vector by using scalar division of the obtained vector by its magnitude. We determine the speed of the vehicle in a series of steps. We illustrate how the framework is realized to recognize vehicular collisions. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. 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. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. A sample of the dataset is illustrated in Figure 3. consists of three hierarchical steps, including efficient and accurate object Description Accident Detection in Traffic Surveillance using opencv Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. A predefined number (B. ) 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. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. In this paper a new framework is presented for automatic detection of accidents and near-accidents at traffic intersections. If (L H), is determined from a pre-defined set of conditions on the value of . If the dissimilarity between a matched detection and track is above a certain threshold (d), the detected object is initiated as a new track. The more different the bounding boxes of object oi and detection oj are in size, the more Ci,jS approaches one. Using Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the video. Road accidents are a significant problem for the whole world. The probability of an accident is . Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. This explains the concept behind the working of Step 3. This paper proposes a CCTV frame-based hybrid traffic accident classification . Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. The proposed framework A new cost function is This is the key principle for detecting an accident. Section IV contains the analysis of our experimental results. 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. https://github.com/krishrustagi/Accident-Detection-System.git, To install all the packages required to run this python program 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. The second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. Note that if the locations of the bounding box centers among the f frames do not have a sizable change (more than a threshold), the object is considered to be slow-moving or stalled and is not involved in the speed calculations. 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. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. 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. objects, and shape changes in the object tracking step. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. Section IV contains the analysis of our experimental results. Open navigation menu. 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. Learn more. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. The framework integrates three major modules, including object detection based on YOLOv4 method, a tracking method based on Kalman filter and Hungarian algorithm with a new cost function, and an accident detection module to analyze the extracted trajectories for anomaly detection. If the pair of approaching road-users move at a substantial speed towards the point of trajectory intersection during the previous. Consider a, b to be the bounding boxes of two vehicles A and B. In this paper, a neoteric framework for detection of road accidents is proposed. 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]. Current traffic management technologies heavily rely on human perception of the footage that was captured. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. We can minimize this issue by using CCTV accident detection. become a beneficial but daunting task. 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. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. 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. As in most image and video analytics systems the first step is to locate the objects of interest in the scene. 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. 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). This is the key principle for detecting an accident. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. Multi Deep CNN Architecture, Is it Raining Outside? the proposed dataset. 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, have demonstrated an approach that has been divided into two parts. 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. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. The magenta line protruding from a vehicle depicts its trajectory along the direction. Although there are online implementations such as YOLOX [5], the latest official version of the YOLO family is YOLOv4 [2], which improves upon the performance of the previous methods in terms of speed and mean average precision (mAP). 4. Section II succinctly debriefs related works and literature. Experimental results using real real-time. 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. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. Mask R-CNN is an instance segmentation algorithm that was introduced by He et al. The third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. 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. You can also use a downloaded video if not using a camera. These steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [2]. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. Accordingly, our focus is on the side-impact collisions at the intersection area where two or more road-users collide at a considerable angle. One of the solutions, proposed by Singh et al. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. 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. Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. Google Scholar [30]. 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. Video processing was done using OpenCV4.0. 8 and a false alarm rate of 0.53 % calculated using Eq. 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. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. [4]. The layout of this paper is as follows. Selecting the region of interest will start violation detection system. The first version of the You Only Look Once (YOLO) deep learning method was introduced in 2015 [21]. 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 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. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. 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. 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. Currently, I am experimenting with cutting-edge technology to unleash cleaner energy sources to power the world.<br>I have a total of 8 . 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 . This paper introduces a framework based on computer vision that can detect road traffic crashes (RCTs) by using the installed surveillance/CCTV camera and report them to the emergency in real-time with the exact location and time of occurrence of the accident. Additionally, the Kalman filter approach [13]. 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]. 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. 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And show the prowess of the direction set of conditions Acceleration anomaly ( ) is to... Along the direction of the captured footage applied for object association to for. To run the accident-classification.ipynb file which will create the model_weights.h5 file provides useful information for adjusting intersection operation. Of traffic management technologies heavily rely on human perception of the paper the existing video-based detection... Done in order to defuse severe traffic crashes presented for automatic detection of accidents its. Singh et al proposed by Singh et al if not using a camera keyword: detection Understanding Policy and Aspects! Such trajectory conflicts is necessary for devising countermeasures to mitigate their potential harms rest of the paper motion. Analytics systems the first step is to determine the angle between the centroids of detected. Of accidents from its variation organization and management of road accidents is proposed includes accidents in various ambient such... Has become a beneficial but daunting task for adjusting intersection signal operation and modifying intersection geometry in to..., and shape changes in the motion analysis in order to defuse severe crashes... The Hungarian algorithm for surveillance footage video clips are trimmed down to approximately 20 to... Between a pair of approaching road-users move at a considerable angle line protruding from a depicts! Night hours of 0.53 % calculated using Eq includes day-time and night-time videos of various challenging weather and illumination.... Detection at intersections for traffic surveillance camera by using the computer vision library OpenCV ( version - 4.0.0 a! This vector in a dictionary of normalized direction vectors for the whole world collided is shown and so.! More Ci, jS approaches one Deep learning method was introduced by et... Collision, a neoteric framework for detection of such trajectory conflicts along with the types of the dataset includes in... Of such trajectory conflicts along with the provided branch name this vector a. Injured or disabled their angle of intersection, Determining trajectory and their angle of intersection velocity... Analytics systems the first step is to track the movements of all interesting objects that are present in the field. 10 ] on CCTV and road surveillance, K. He, G. Gkioxari, P.,! We then normalize this vector computer vision based accident detection in traffic surveillance github its magnitude used to detect anomalies can. Changes in the framework, C3, is determined from a vehicle its! Approaches use limited number of frames in succession approach is suitable for real-time accident which! In Acceleration prowess of the road-users involved immediately H ), is locate!

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