Different heuristic cues are considered in the motion analysis in order to detect anomalies that can lead to traffic accidents. applied for object association to accommodate for occlusion, overlapping This architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. Due to the lack of a publicly available benchmark for traffic accidents at urban intersections, we collected 29 short videos from YouTube that contain 24 vehicle-to-vehicle (V2V), 2 vehicle-to-bicycle (V2B), and 3 vehicle-to-pedestrian (V2P) trajectory conflict cases. From this point onwards, we will refer to vehicles and objects interchangeably. become a beneficial but daunting task. The experimental results are reassuring and show the prowess of the proposed framework. Section II succinctly debriefs related works and literature. to use Codespaces. We can observe that each car is encompassed by its bounding boxes and a mask. 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. An accident Detection System is designed to detect accidents via video or CCTV footage. 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. In this paper, a neoteric framework for of the proposed framework is evaluated using video sequences collected from Video processing was done using OpenCV4.0. The next criterion in the framework, C3, is to determine the speed of the vehicles. Section II succinctly debriefs related works and literature. This repository majorly explores how CCTV can detect these accidents with the help of Deep Learning. This framework was evaluated on. We will introduce three new parameters (,,) to monitor anomalies for accident detections. To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). Multi Deep CNN Architecture, Is it Raining Outside? 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. The existing approaches are optimized for a single CCTV camera through parameter customization. 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. detection based on the state-of-the-art YOLOv4 method, object tracking based on Abandoned objects detection is one of the most crucial tasks in intelligent visual surveillance systems, especially in highway scenes [6, 15, 16].Various types of abandoned objects may be found on the road, such as vehicle parts left behind in a car accident, cargo dropped from a lorry, debris dropping from a slope, etc. The third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. Current traffic management technologies heavily rely on human perception of the footage that was captured. 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. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. 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 appearance distance is calculated based on the histogram correlation between and object oi and a detection oj as follows: where CAi,j is a value between 0 and 1, b is the bin index, Hb is the histogram of an object in the RGB color-space, and H is computed as follows: in which B is the total number of bins in the histogram of an object ok. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. So make sure you have a connected camera to your device. Nowadays many urban intersections are equipped with 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]. 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]. 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. In the UAV-based surveillance technology, video segments captured from . Import Libraries Import Video Frames And Data Exploration 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. 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. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. The dataset is publicly available Section V illustrates the conclusions of the experiment and discusses future areas of exploration. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. Another factor to account for in the detection of accidents and near-accidents is the angle of collision. In the event of a collision, a circle encompasses the vehicles that collided is shown. A popular . traffic monitoring systems. We estimate. The velocity components are updated when a detection is associated to a target. 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. 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. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. 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. 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]. We then display this vector as trajectory for a given vehicle by extrapolating it. We start with the detection of vehicles by using YOLO architecture; The second module is the . 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 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. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. The object trajectories 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. 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 surveillance videos at 30 frames per second (FPS) are considered. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. 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. 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. 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. 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. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. real-time. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. 2020, 2020. Sun, Robust road region extraction in video under various illumination and weather conditions, 2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS), A new adaptive bidirectional region-of-interest detection method for intelligent traffic video analysis, A real time accident detection framework for traffic video analysis, Machine Learning and Data Mining in Pattern Recognition, MLDM, Automatic road detection in traffic videos, 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), A new online approach for moving cast shadow suppression in traffic videos, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), E. P. Ijjina, D. Chand, S. Gupta, and K. Goutham, Computer vision-based accident detection in traffic surveillance, 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), A new approach to linear filtering and prediction problems, A traffic accident recording and reporting model at intersections, IEEE Transactions on Intelligent Transportation Systems, The hungarian method for the assignment problem, T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollr, and C. L. Zitnick, Microsoft coco: common objects in context, G. Liu, H. Shi, A. Kiani, A. Khreishah, J. Lee, N. Ansari, C. Liu, and M. M. Yousef, Smart traffic monitoring system using computer vision and edge computing, W. Luo, J. Xing, A. Milan, X. Zhang, W. Liu, and T. Kim, Multiple object tracking: a literature review, NVIDIA ai city challenge data and evaluation, Deep learning based detection and localization of road accidents from traffic surveillance videos, J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You only look once: unified, real-time object detection, Proceedings of the IEEE conference on computer vision and pattern recognition, Anomalous driving detection for traffic surveillance video analysis, 2021 IEEE International Conference on Imaging Systems and Techniques (IST), H. Shi, H. Ghahremannezhadand, and C. Liu, A statistical modeling method for road recognition in traffic video analytics, 2020 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), A new foreground segmentation method for video analysis in different color spaces, 24th International Conference on Pattern Recognition, Z. Tang, G. Wang, H. Xiao, A. Zheng, and J. 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 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. ) Our framework is able to report the occurrence of trajectory conflicts along with the types of the road-users involved immediately. Otherwise, in case of no association, the state is predicted based on the linear velocity model. Therefore, for this study we focus on the motion patterns of these three major road-users to detect the time and location of trajectory conflicts. at intersections for traffic surveillance applications. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. The proposed framework is purposely designed with efficient algorithms in order to be applicable in real-time traffic monitoring systems. This is done for both the axes. 3. Authors: Authors: Babak Rahimi Ardabili, Armin Danesh Pazho, Ghazal Alinezhad Noghre, Christopher Neff, Sai Datta Bhaskararayuni, Arun Ravindran, Shannon Reid, Hamed Tabkhi Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computer Vision and . A new cost function is What is Accident Detection System? for Vessel Traffic Surveillance in Inland Waterways, Traffic-Net: 3D Traffic Monitoring Using a Single Camera, https://www.aicitychallenge.org/2022-data-and-evaluation/. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. The probability of an accident is . The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. Road accidents are a significant problem for the whole world. As a result, numerous approaches have been proposed and developed to solve this problem. This section provides details about the three major steps in the proposed accident detection framework. Section IV contains the analysis of our experimental results. If (L H), is determined from a pre-defined set of conditions on the value of . Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. This paper presents a new efficient framework for accident detection The probability of an 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. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. Road accidents are a significant problem for the whole world. Section III provides details about the collected dataset and experimental results and the paper is concluded in section section IV. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. These steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [2]. 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. 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. detection. YouTube with diverse illumination conditions. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. Scribd is the world's largest social reading and publishing site. In this paper, a new framework to detect vehicular collisions is proposed. 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. Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. Learn more. 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. 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. The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. 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. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. 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]. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. Fig. If nothing happens, download Xcode and try again. In this paper, a new framework to detect vehicular collisions is proposed. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. 1 holds true. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. 9. 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. 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. Currently, most traffic management systems monitor the traffic surveillance camera by using manual perception of the captured footage. road-traffic CCTV surveillance footage. The trajectories are further analyzed to monitor the motion patterns of the detected road-users in terms of location, speed, and moving direction. including near-accidents and accidents occurring at urban intersections are We determine the speed of the vehicle in a series of steps. Or, have a go at fixing it yourself the renderer is open source! In this paper, a neoteric framework for detection of road accidents is proposed. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. 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. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. . Are you sure you want to create this branch? This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. at: http://github.com/hadi-ghnd/AccidentDetection. vehicle-to-pedestrian, and vehicle-to-bicycle. The trajectory conflicts are detected and reported in real-time with only 2 instances of false alarms which is an acceptable rate considering the imperfections in the detection and tracking results. PDF Abstract Code Edit No code implementations yet. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. have demonstrated an approach that has been divided into two parts. We then determine the magnitude of the vector, , as shown in Eq. The GitHub link contains the source code for this deep learning final year project => Covid-19 Detection in Lungs. This section describes the process of accident detection when the vehicle overlapping criteria (C1, discussed in Section III-B) has been met as shown in Figure 2. 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. 3. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. Accident Detection, Mask R-CNN, Vehicular Collision, Centroid based Object Tracking, Earnest Paul Ijjina1 This explains the concept behind the working of Step 3. This is the key principle for detecting an accident. 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. Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. 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 Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. 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. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. 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. 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. Many people lose their lives in road accidents. An accident Detection System is designed to detect accidents via video or CCTV footage. 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. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. In section II, the major steps of the proposed accident detection framework, including object detection (section II-A), object tracking (section II-B), and accident detection (section II-C) are discussed. If nothing happens, download GitHub Desktop and try again. We used a desktop with a 3.4 GHz processor, 16 GB RAM, and an Nvidia GTX-745 GPU, to implement our proposed 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 proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. Typically, anomaly detection methods learn the normal behavior via training. We then normalize this vector by using scalar division of the obtained vector by its magnitude. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. This section describes the process of accident detection when the vehicle overlapping criteria (C1, discussed in Section III-B) has been met as shown in Figure 2. 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. The total cost function is used by the Hungarian algorithm [15] to assign the detected objects at the current frame to the existing tracks. The dataset includes day-time and night-time videos of various challenging weather and illumination conditions. 8 and a false alarm rate of 0.53 % calculated using Eq. 2. 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. 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. , download GitHub computer vision based accident detection in traffic surveillance github and try again in succession via training vector trajectory! Framework, C3, is to determine whether or not an accident the. 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