The family of YOLO-based deep learning methods demonstrates the best compromise between efficiency and performance among object detectors. 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. The surveillance videos at 30 frames per second (FPS) are considered. The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. 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. In this . The probability of an accident is . We start with the detection of vehicles by using YOLO architecture; The second module is the . 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 architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. Using Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the video. The recent motion patterns of each pair of close objects are examined in terms of speed and moving direction. 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. 3. The dataset includes day-time and night-time videos of various challenging weather and illumination conditions. 4. The proposed framework achieved a detection rate of 71 % calculated using Eq. If (L H), is determined from a pre-defined set of conditions on the value of . The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. arXiv as responsive web pages so you The layout of this paper is as follows. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. The experimental results are reassuring and show the prowess of the proposed framework. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. Note: This project requires a camera. 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. Similarly, Hui et al. 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. Dhananjai Chand2, Savyasachi Gupta 3, Goutham K 4, Assistant Professor, Department of Computer Science and Engineering, B.Tech., Department of Computer Science and Engineering, Results, Statistics and Comparison with Existing models, F. Baselice, G. Ferraioli, G. Matuozzo, V. Pascazio, and G. Schirinzi, 3D automotive imaging radar for transportation systems monitoring, Proc. Accordingly, our focus is on the side-impact collisions at the intersection area where two or more road-users collide at a considerable angle. Recently, traffic accident detection is becoming one of the interesting fields due to its tremendous application potential in Intelligent . The third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. 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]. 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. different types of trajectory conflicts including vehicle-to-vehicle, Many people lose their lives in road accidents. In the event of a collision, a circle encompasses the vehicles that collided is shown. What is Accident Detection System? To contribute to this project, knowledge of basic python scripting, Machine Learning, and Deep Learning will help. A popular . Each video clip includes a few seconds before and after a trajectory conflict. 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. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. 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. The velocity components are updated when a detection is associated to a target. Description Accident Detection in Traffic Surveillance using opencv Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. Want to hear about new tools we're making? This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. 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. 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. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. 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]. Kalman filter coupled with the Hungarian algorithm for association, and Computer vision-based accident detection through video surveillance has The spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. We then normalize this vector by using scalar division of the obtained vector by its magnitude. 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 Before running the program, you need to run the accident-classification.ipynb file which will create the model_weights.h5 file. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. PDF Abstract Code Edit No code implementations yet. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. 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. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. Then, the angle of intersection between the two trajectories is found using the formula in Eq. Consider a, b to be the bounding boxes of two vehicles A and B. A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. traffic video data show the feasibility of the proposed method in real-time 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. Then the approaching angle of the a pair of road-users a and b is calculated as follows: where denotes the estimated approaching angle, ma and mb are the the general moving slopes of the road-users a and b with respect to the origin of the video frame, xta, yta, xtb, ytb represent the center coordinates of the road-users a and b at the current frame, xta and yta are the center coordinates of object a when first observed, xtb and ytb are the center coordinates of object b when first observed, respectively. Mask R-CNN not only provides the advantages of Instance Segmentation but also improves the core accuracy by using RoI Align algorithm. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. In the event of a collision, a circle encompasses the vehicles that collided is shown. computer vision techniques can be viable tools for automatic accident The performance is compared to other representative methods in table I. 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. Video processing was done using OpenCV4.0. Traffic accidents include different scenarios, such as rear-end, side-impact, single-car, vehicle rollovers, or head-on collisions, each of which contain specific characteristics and motion patterns. Work fast with our official CLI. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. Section IV contains the analysis of our experimental results. Nowadays many urban intersections are equipped with including near-accidents and accidents occurring at urban intersections are 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. 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. 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. 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. 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. 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. 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. Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. Current traffic management technologies heavily rely on human perception of the footage that was captured. 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.
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