The robustness De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. This architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. The proposed framework capitalizes on The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. 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. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. Current traffic management technologies heavily rely on human perception of the footage that was captured. 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. The third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. 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. We then display this vector as trajectory for a given vehicle by extrapolating it. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. One of the solutions, proposed by Singh et al. 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. Scribd is the world's largest social reading and publishing site. Furthermore, Figure 5 contains samples of other types of incidents detected by our framework, including near-accidents, vehicle-to-bicycle (V2B), and vehicle-to-pedestrian (V2P) conflicts. One of the main problems in urban traffic management is the conflicts and accidents occurring at the intersections. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. This framework was found effective and paves the way to Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. computer vision techniques can be viable tools for automatic accident Our framework is able to report the occurrence of trajectory conflicts along with the types of the road-users involved immediately. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure 1. 9. Using Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the video. 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. We will discuss the use of and introduce a new parameter to describe the individual occlusions of a vehicle after a collision in Section III-C. Let's first import the required libraries and the modules. 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. 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. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. detected with a low false alarm rate and a high detection rate. If you find a rendering bug, file an issue on GitHub. To use this project Python Version > 3.6 is recommended. Before running the program, you need to run the accident-classification.ipynb file which will create the model_weights.h5 file. 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. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. 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 performance is compared to other representative methods in table I. for Vessel Traffic Surveillance in Inland Waterways, Traffic-Net: 3D Traffic Monitoring Using a Single Camera, https://www.aicitychallenge.org/2022-data-and-evaluation/. We estimate , the interval between the frames of the video, using the Frames Per Second (FPS) as given in Eq. 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 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 more different the bounding boxes of object oi and detection oj are in size, the more Ci,jS approaches one. The spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. The existing approaches are optimized for a single CCTV camera through parameter customization. Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. Since most intersections are equipped with surveillance cameras automatic detection of traffic accidents based on computer vision technologies will mean a great deal to traffic monitoring systems. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. 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 next task in the framework, T2, is to determine the trajectories of the vehicles. 2020, 2020. We then determine the magnitude of the vector. We then normalize this vector by using scalar division of the obtained vector by its magnitude. 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. vehicle-to-pedestrian, and vehicle-to-bicycle. Note: This project requires a camera. Similarly, Hui et al. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. An accident Detection System is designed to detect accidents via video or CCTV footage. , to locate and classify the road-users at each video frame. A new cost function is Computer vision-based accident detection through video surveillance has 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. 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). This explains the concept behind the working of Step 3. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. The proposed framework of the proposed framework is evaluated using video sequences collected from 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. 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. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. 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. 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. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. arXiv Vanity renders academic papers from This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. Consider a, b to be the bounding boxes of two vehicles A and B. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. 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. 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. 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. 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. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. The object trajectories This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. We then determine the magnitude of the vector, , as shown in Eq. , " A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition," Journal of advanced transportation, vol. The object detection and object tracking modules are implemented asynchronously to speed up the calculations. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. 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. detection of road accidents is proposed. Additionally, the Kalman filter approach [13]. The layout of the rest of the paper is as follows. 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. 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. Are you sure you want to create this branch? The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. 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 consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. This paper presents a new efficient framework for accident detection This repository majorly explores how CCTV can detect these accidents with the help of Deep Learning. method to achieve a high Detection Rate and a low False Alarm Rate on general Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. The dataset includes day-time and night-time videos of various challenging weather and illumination conditions. Road accidents are a significant problem for the whole world. 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. 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. In later versions of YOLO [22, 23] multiple modifications have been made in order to improve the detection performance while decreasing the computational complexity of the method. All the experiments conducted in relation to this framework validate the potency and efficiency of the proposition and thereby authenticates the fact that the framework can render timely, valuable information to the concerned authorities. The framework is built of five modules. Add a In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. 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. Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. These steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [2]. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Then, to run this python program, you need to execute the main.py python file. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. For everything else, email us at [emailprotected]. 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. Each video clip includes a few seconds before and after a trajectory conflict. 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 probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. 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. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. Timely detection of such trajectory conflicts is necessary for devising countermeasures to mitigate their potential harms. 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 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]. We will be using the computer vision library OpenCV (version - 4.0.0) a lot in this implementation. The results are evaluated by calculating Detection and False Alarm Rates as metrics: The proposed framework achieved a Detection Rate of 93.10% and a False Alarm Rate of 6.89%. Automatic detection of traffic accidents is an important emerging topic in The moving direction and speed of road-user pairs that are close to each other are examined based on their trajectories in order to detect anomalies that can cause them to crash. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. 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. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. 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. As a result, numerous approaches have been proposed and developed to solve this problem. Our approach included creating a detection model, followed by anomaly detection and . This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. The family of YOLO-based deep learning methods demonstrates the best compromise between efficiency and performance among object detectors. 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computer vision based accident detection in traffic surveillance github