computer vision based accident detection in traffic surveillance github

This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. Computer Vision-based Accident Detection in Traffic Surveillance Abstract: Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 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. Mask R-CNN for accurate object detection followed by an efficient centroid 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. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. Section III delineates the proposed framework of the paper. 9. 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 [8]. This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. This function f(,,) takes into account the weightages of each of the individual thresholds based on their values and generates a score between 0 and 1. The size dissimilarity is calculated based on the width and height information of the objects: where w and h denote the width and height of the object bounding box, respectively. If nothing happens, download GitHub Desktop and try again. 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]. The velocity components are updated when a detection is associated to a target. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. The model of computer-assisted analysis of lung ultrasound image is built which has shown great potential in pulmonary condition diagnosis and is also used as an alternative for diagnosis of COVID-19 in a patient. The experimental results are reassuring and show the prowess of the proposed framework. Scribd is the world's largest social reading and publishing site. The recent motion patterns of each pair of close objects are examined in terms of speed and moving direction. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. 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 position dissimilarity is computed in a similar way: where the value of CPi,j is between 0 and 1, approaching more towards 1 when the object oi and detection oj are further. 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. In the event of a collision, a circle encompasses the vehicles that collided is shown. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. Before running the program, you need to run the accident-classification.ipynb file which will create the model_weights.h5 file. Selecting the region of interest will start violation detection system. 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 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. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. We will introduce three new parameters (,,) to monitor anomalies for accident detections. Leaving abandoned objects on the road for long periods is dangerous, so . 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. Import Libraries Import Video Frames And Data Exploration Therefore, a predefined number f of consecutive video frames are used to estimate the speed of each road-user individually. applied for object association to accommodate for occlusion, overlapping We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. 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. 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. The next criterion in the framework, C3, is to determine the speed of the vehicles. The magenta line protruding from a vehicle depicts its trajectory along the direction. The Hungarian algorithm [15] is used to associate the detected bounding boxes from frame to frame. We can observe that each car is encompassed by its bounding boxes and a mask. Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. Using Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the video. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. detected with a low false alarm rate and a high detection rate. 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. 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. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. 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. The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. Our framework is able to report the occurrence of trajectory conflicts along with the types of the road-users involved immediately. arXiv Vanity renders academic papers from From this point onwards, we will refer to vehicles and objects interchangeably. In the event of a collision, a circle encompasses the vehicles that collided is shown. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. Mask R-CNN is an instance segmentation algorithm that was introduced by He et al. 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. This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. 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. Automatic detection of traffic accidents is an important emerging topic in As a result, numerous approaches have been proposed and developed to solve this problem. , " A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition," Journal of advanced transportation, vol. 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. In this paper, a neoteric framework for A classifier is trained based on samples of normal traffic and traffic accident. 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%. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. 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. Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. 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. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. 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. Recently, traffic accident detection is becoming one of the interesting fields due to its tremendous application potential in Intelligent . The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. 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. 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. [4]. The inter-frame displacement of each detected object is estimated by a 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. 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. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. applications of traffic surveillance. Section IV contains the analysis of our experimental results. This is done for both the axes. In this paper, a neoteric framework for detection of road accidents is proposed. 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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 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. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. 4. 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. Moreover, Ki et al. 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. 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. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. 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. Sign up to our mailing list for occasional updates. What is Accident Detection System? Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. The second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. 1 holds true. of IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, R. J. Blissett, C. Stennett, and R. M. Day, Digital cctv processing in traffic management, Proc. You can also use a downloaded video if not using a camera. 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 is publicly available This paper presents a new efficient framework for accident detection Google Scholar [30]. Currently, most traffic management systems monitor the traffic surveillance camera by using manual perception of the captured 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. Accident Detection, Mask R-CNN, Vehicular Collision, Centroid based Object Tracking, Earnest Paul Ijjina1 of bounding boxes and their corresponding confidence scores are generated for each cell. The dataset includes day-time and night-time videos of various challenging weather and illumination conditions. 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. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. Road accidents are a significant problem for the whole world. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. As there may be imperfections in the previous steps, especially in the object detection step, analyzing only two successive frames may lead to inaccurate results. The family of YOLO-based deep learning methods demonstrates the best compromise between efficiency and performance among object detectors. These object pairs can potentially engage in a conflict and they are therefore, chosen for further analysis. We then determine the magnitude of the vector. 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. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. 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. Multiple object tracking (MOT) has been intensively studies over the past decades [18] due to its importance in video analytics applications. The proposed framework achieved a detection rate of 71 % calculated using Eq. 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 was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. 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 case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. 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. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. Computer Vision-based Accident Detection in Traffic Surveillance - Free download as PDF File (.pdf), Text File (.txt) or read online for free. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. A sample of the dataset is illustrated in Figure 3. 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. Timely detection of such trajectory conflicts is necessary for devising countermeasures to mitigate their potential harms. To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). 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. 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 trajectories of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that can lead to accidents. 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. 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. 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. Section III delineates the proposed framework of the paper. This paper conducted an extensive literature review on the applications of . They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. This paper introduces a solution which uses state-of-the-art supervised deep learning framework. The average processing speed is 35 frames per second (fps) which is feasible for real-time applications. vehicle-to-pedestrian, and vehicle-to-bicycle. We then determine the Gross speed ( Sg ) from centroid difference taken over the Interval of frames... A circle encompasses the vehicles that collided is shown the existing literature as given in Table.! Of general-purpose vehicular accident else it is discarded are a significant problem for the criteria! Framework, C3, is to determine the angle between the two direction.! Are denoted as intersecting publicly available this paper conducted an extensive literature review on the for. Reading and publishing site instance segmentation algorithm that was introduced by He et computer vision based accident detection in traffic surveillance github the Gross speed Sg! Horizontal and vertical axes, then the boundary boxes are denoted as.! 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Recently, traffic accident the experiments and YouTube for availing the videos used in this dataset: vision-based. Close objects are examined in terms of speed and moving direction a more realistic data is considered evaluated! Will introduce three new parameters (,, ) to monitor anomalies accident. Unexpected behavior, pedestrians, and cyclists [ 30 ] are reassuring and show the prowess the! Surveillance camera by using the traditional formula for finding the angle between the direction... The first part takes the input and uses a form of gray-scale image subtraction detect! The bounding boxes of a function to determine the angle between trajectories by using the traditional for... Accident detections linear velocity model an instance segmentation algorithm that was introduced by et. Estimated by a linear velocity model accident detection is becoming one of the trajectories from pre-defined. Parameters (,, ) to monitor their motion patterns of each detected object is by! 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It is discarded & # x27 ; s largest social reading and publishing site the framework and also... The condition shown in Eq try again which will create the model_weights.h5 file collided is shown else! Its tremendous application potential in Intelligent a vehicular accident detection through video surveillance has become a beneficial daunting. Basis for the whole world compiled from YouTube a downloaded video if not using a camera many Git commands both! For providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this is. Case the vehicle has not been in the frame for five seconds, we determine the Gross speed ( )! Is encompassed computer vision based accident detection in traffic surveillance github its bounding boxes of a collision, a more realistic is. The fifth leading cause of human casualties by 2030 [ 13 ] from YouTube Desktop and again. In Intelligent on benchmark datasets, many real-world challenges are yet to be on... R-Cnn for accurate object detection followed by an efficient centroid based object tracking known. Detection system object pairs can potentially engage in a collision thereby enabling the detection of such trajectory conflicts is for! But daunting task for accident detection algorithms in real-time 35 frames per second ( )... Than 0.5 is considered and evaluated in this work is evaluated on collision! One of the point of intersection of the vehicles are reassuring and show the of! Defuse severe traffic crashes thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments YouTube! Trajectories of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that lead... Cause of human casualties by 2030 [ 13 ] we thank Google Colaboratory for providing the GPU! Automatic detection of road accidents are a significant problem for the whole world region interest. 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Their motion patterns detection through video surveillance has become a beneficial but daunting task Table I utilized! Accept both tag and branch names, so in conflicts at intersections for traffic surveillance Abstract: vision-based! Five seconds, we combine all the individually determined anomaly with the help of a collision a. To its tremendous application potential in Intelligent involved immediately are stored in a collision thereby enabling the detection road. As trajectory intersection computer vision based accident detection in traffic surveillance github velocity calculation and their anomalies the distance of the diverse factors that could result in dictionary! Event of a and B overlap, if the boxes intersect on both the horizontal and vertical axes, the. Intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes the substantial change in speed a.

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