We can observe that each car is encompassed by its bounding boxes and a mask. , " A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition," Journal of advanced transportation, vol. 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. The proposed framework achieved a detection rate of 71 % calculated using Eq. The object trajectories The next criterion in the framework, C3, is to determine the speed of the vehicles. 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. 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. 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. What is Accident Detection System? The Overlap of bounding boxes of two vehicles plays a key role in this framework. Currently, most traffic management systems monitor the traffic surveillance camera by using manual perception of the captured footage. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 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. As in most image and video analytics systems the first step is to locate the objects of interest in the scene. In this paper, a new framework to detect vehicular collisions is proposed. https://github.com/krishrustagi/Accident-Detection-System.git, To install all the packages required to run this python program In this paper, a neoteric framework for detection of road accidents is proposed. In computer vision, anomaly detection is a sub-field of behavior understanding from surveillance scenes. Detection of Rainfall using General-Purpose Section V illustrates the conclusions of the experiment and discusses future areas of exploration. detection based on the state-of-the-art YOLOv4 method, object tracking based on 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. 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. Let's first import the required libraries and the modules. The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. 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. Computer Vision-based Accident Detection in Traffic Surveillance - Free download as PDF File (.pdf), Text File (.txt) or read online for free. Many people lose their lives in road accidents. The proposed framework provides a robust 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. 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. Note: This project requires a camera. 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. Google Scholar [30]. In the event of a collision, a circle encompasses the vehicles that collided is shown. detection. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. As a result, numerous approaches have been proposed and developed to solve this problem. Nowadays many urban intersections are equipped with 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. Typically, anomaly detection methods learn the normal behavior via training. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. Kalman filter coupled with the Hungarian algorithm for association, and 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 Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. 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. In this section, details about the heuristics used to detect conflicts between a pair of road-users are presented. In the event of a collision, a circle encompasses the vehicles that collided is shown. We will introduce three new parameters (,,) to monitor anomalies for accident detections. To use this project Python Version > 3.6 is recommended. 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. 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 Overlap of bounding boxes of two vehicles plays a key role in this framework. An accident Detection System is designed to detect accidents via video or CCTV footage. The second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. These steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [2]. Anomalies are typically aberrations of scene entities (people, vehicles, environment) and their interactions from normal behavior. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. Once the vehicles have been detected in a given frame, the next imperative task of the framework is to keep track of each of the detected objects in subsequent time frames of the footage. 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. We then normalize this vector by using scalar division of the obtained vector by its magnitude. arXiv Vanity renders academic papers from 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. arXiv as responsive web pages so you The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. One of the main problems in urban traffic management is the conflicts and accidents occurring at the intersections. 8 and a false alarm rate of 0.53 % calculated using Eq. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. 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. Otherwise, we discard it. In the UAV-based surveillance technology, video segments captured from . The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. The velocity components are updated when a detection is associated to a target. This paper introduces a solution which uses state-of-the-art supervised deep learning framework. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. 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. We start with the detection of vehicles by using YOLO architecture; The second module is the . This results in a 2D vector, representative of the direction of the vehicles motion. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. The experimental results are reassuring and show the prowess of the proposed framework. 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. This is done for both the axes. Otherwise, in case of no association, the state is predicted based on the linear velocity model. 3. The proposed framework is purposely designed with efficient algorithms in order to be applicable in real-time traffic monitoring systems. different types of trajectory conflicts including vehicle-to-vehicle, 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. 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. A predefined number (B. ) Accident Detection, Mask R-CNN, Vehicular Collision, Centroid based Object Tracking, Earnest Paul Ijjina1 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. This is the key principle for detecting an accident. Mask R-CNN not only provides the advantages of Instance Segmentation but also improves the core accuracy by using RoI Align algorithm. The total cost function is used by the Hungarian algorithm [15] to assign the detected objects at the current frame to the existing tracks. 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. 3. 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]. 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 paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. In this paper, a new framework to detect vehicular collisions is proposed. This paper presents a new efficient framework for accident detection at intersections . 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. Here we employ a simple but effective tracking strategy similar to that of the Simple Online and Realtime Tracking (SORT) approach [1]. The existing approaches are optimized for a single CCTV camera through parameter customization. 1: The system architecture of our proposed accident detection framework. Therefore, This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. 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. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. 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. of the proposed framework is evaluated using video sequences collected from 2. 9. 1 holds true. The inter-frame displacement of each detected object is estimated by a linear velocity model. 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. 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. Before running the program, you need to run the accident-classification.ipynb file which will create the model_weights.h5 file. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. Similarly, Hui et al. Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. The proposed framework capitalizes on 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. We determine the speed of the vehicle in a series of steps. 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. 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. 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. Computer Vision-based Accident Detection in Traffic Surveillance Abstract: Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. This explains the concept behind the working of Step 3. Open navigation menu. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. The next task in the framework, T2, is to determine the trajectories of the vehicles. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. A sample of the dataset is illustrated in Figure 3. 1 holds true. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. The layout of the rest of the paper is as follows. We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. 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 family of YOLO-based deep learning methods demonstrates the best compromise between efficiency and performance among object detectors. 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. 2020, 2020. 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. Computer vision -based accident detection through video surveillance has become a beneficial but daunting task. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure 1. A tag already exists with the provided branch name. Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. Are you sure you want to create this branch? 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 capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. To contribute to this project, knowledge of basic python scripting, Machine Learning, and Deep Learning will help. The object detection and object tracking modules are implemented asynchronously to speed up the calculations. 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. Then, we determine the distance covered by a vehicle over five frames from the centroid of the vehicle c1 in the first frame and c2 in the fifth frame. The surveillance videos at 30 frames per second (FPS) are considered. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. 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. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. This repository majorly explores how CCTV can detect these accidents with the help of Deep Learning. Pawar K. and Attar V., " Deep learning based detection and localization of road accidents from traffic surveillance videos," ICT Express, 2021. We then display this vector as trajectory for a given vehicle by extrapolating it. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. Section II succinctly debriefs related works and literature. detected with a low false alarm rate and a high detection rate. From this point onwards, we will refer to vehicles and objects interchangeably. This is the key principle for detecting an accident. In addition to the mentioned dissimilarity measures, we also use the IOU value to calculate the Jaccard distance as follows: where Box(ok) denotes the set of pixels contained in the bounding box of object k. The overall dissimilarity value is calculated as a weighted sum of the four measures: in which wa, ws, wp, and wk define the contribution of each dissimilarity value in the total cost function. The GitHub link contains the source code for this deep learning final year project => Covid-19 Detection in Lungs. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. This paper conducted an extensive literature review on the applications of . This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. sign in Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. As illustrated in fig. This architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. Numerous studies have applied computer vision techniques in traffic surveillance systems [26, 17, 9, 7, 6, 25, 8, 3, 10, 24] for various tasks. detection of road accidents is proposed. Then, the angle of intersection between the two trajectories is found using the formula in Eq. We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. Hence, this paper proposes a pragmatic solution for addressing aforementioned problem by suggesting a solution to detect Vehicular Collisions almost spontaneously which is vital for the local paramedics and traffic departments to alleviate the situation in time. One of the solutions, proposed by Singh et al. A dataset of various traffic videos containing accident or near-accident scenarios is collected to test the performance of the proposed framework against real videos. 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. 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]. 8 and a false alarm rate of 0.53 % calculated using Eq. to use Codespaces. 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. after an overlap with other vehicles. 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. If nothing happens, download Xcode and try again. 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. Different heuristic cues are considered in the motion analysis in order to detect anomalies that can lead to traffic accidents. With efficient algorithms in order to defuse severe traffic crashes and discusses future of. Linear velocity model this point onwards, we find the acceleration of the solutions proposed... Learning final year project = & gt ; Covid-19 detection in traffic surveillance applications each detected object estimated... Annual basis with an additional 20-50 million injured or disabled in urban areas where people commute customarily link! Of road-users are presented to monitor anomalies for accident detection at intersections for traffic camera... Segments captured from the intersections seen in Figure 3 traffic crashes understanding surveillance... 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