This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. Therefore, computer vision techniques can be viable tools for automatic accident detection. The family of YOLO-based deep learning methods demonstrates the best compromise between efficiency and performance among object detectors. real-time. The existing approaches are optimized for a single CCTV camera through parameter customization. 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. Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. based object tracking algorithm for surveillance footage. 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. If you find a rendering bug, file an issue on GitHub. 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. different types of trajectory conflicts including vehicle-to-vehicle, This section provides details about the three major steps in the proposed accident detection framework. The layout of this paper is as follows. Computer Vision-based Accident Detection in Traffic Surveillance Abstract: Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. 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. 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. After that administrator will need to select two points to draw a line that specifies traffic signal. This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. In computer vision, anomaly detection is a sub-field of behavior understanding from surveillance scenes. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. have demonstrated an approach that has been divided into two parts. Otherwise, in case of no association, the state is predicted based on the linear velocity model. Although there are online implementations such as YOLOX [5], the latest official version of the YOLO family is YOLOv4 [2], which improves upon the performance of the previous methods in terms of speed and mean average precision (mAP). 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 capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage to achieve a high Detection Rate and a low False Alarm Rate on general road-traffic CCTV surveillance footage. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. Work fast with our official CLI. Open navigation menu. In this paper, a new framework to detect vehicular collisions is proposed. objects, and shape changes in the object tracking step. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. 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. In this paper, a neoteric framework for detection of road accidents is proposed. Therefore, a predefined number f of consecutive video frames are used to estimate the speed of each road-user individually. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. We then display this vector as trajectory for a given vehicle by extrapolating it. 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 framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. 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. Other dangerous behaviors, such as sudden lane changing and unpredictable pedestrian/cyclist movements at the intersection, may also arise due to the nature of traffic control systems or intersection geometry. Consider a, b to be the bounding boxes of two vehicles A and B. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. The main idea of this method is to divide the input image into an SS grid where each grid cell is either considered as background or used for the detecting an object. The next task in the framework, T2, is to determine the trajectories of the vehicles. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. 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. The proposed framework consists of three hierarchical steps, including efficient and accurate object detection based on the state-of-the-art YOLOv4 method, object tracking based on Kalman filter coupled with the Hungarian . The proposed framework provides a robust 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. 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 layout of the rest of the paper is as follows. We then determine the magnitude of the vector, , as shown in Eq. 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, 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 process used to determine, where the bounding boxes of two vehicles overlap goes as follow: Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. Computer Vision-based Accident Detection in Traffic Surveillance Earnest Paul Ijjina, Dhananjai Chand, Savyasachi Gupta, Goutham K Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Nowadays many urban intersections are equipped with The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: Moreover, Ki et al. 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. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. The surveillance videos at 30 frames per second (FPS) are considered. The trajectories of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that can lead to accidents. 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. Otherwise, we discard it. These steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [2]. 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. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. The surveillance videos at 30 frames per second (FPS) are considered. This framework was found effective and paves the way to 7. First, the Euclidean distances among all object pairs are calculated in order to identify the objects that are closer than a threshold to each other. Google Scholar [30]. A predefined number (B. ) The magenta line protruding from a vehicle depicts its trajectory along the direction. This paper introduces a solution which uses state-of-the-art supervised deep learning framework. The proposed framework consists of three hierarchical steps, including . Learn more. There was a problem preparing your codespace, please try again. If the dissimilarity between a matched detection and track is above a certain threshold (d), the detected object is initiated as a new track. 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. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. The index i[N]=1,2,,N denotes the objects detected at the previous frame and the index j[M]=1,2,,M represents the new objects detected at the current frame. The bounding box centers of each road-user are extracted at two points: (i) when they are first observed and (ii) at the time of conflict with another road-user. A tag already exists with the provided branch name. Computer Vision-based Accident Detection in Traffic Surveillance - Free download as PDF File (.pdf), Text File (.txt) or read online for free. 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]. Section IV contains the analysis of our experimental results. 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. One of the solutions, proposed by Singh et al. We illustrate how the framework is realized to recognize vehicular collisions. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. Figure 4 shows sample accident detection results by our framework given videos containing vehicle-to-vehicle (V2V) side-impact collisions. This is done for both the axes. Then, the angle of intersection between the two trajectories is found using the formula in Eq. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other 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. Then, to run this python program, you need to execute the main.py python file. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. 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. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. Add a 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. This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. Accordingly, our focus is on the side-impact collisions at the intersection area where two or more road-users collide at a considerable angle. To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). This framework was evaluated on. 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. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. 1 holds true. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. The proposed framework capitalizes on If (L H), is determined from a pre-defined set of conditions on the value of . Current traffic management technologies heavily rely on human perception of the footage that was captured. 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. pip install -r requirements.txt. A sample of the dataset is illustrated in Figure 3. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. 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, a neoteric framework for detection of road accidents is proposed. This repository majorly explores how CCTV can detect these accidents with the help of Deep Learning. 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. 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. In this paper a new framework is presented for automatic detection of accidents and near-accidents at traffic intersections. 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 dataset is publicly available This section describes our proposed framework given in Figure 2. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. 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. 3. 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. 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. Leaving abandoned objects on the road for long periods is dangerous, so . Surveillance, Detection of road traffic crashes based on collision estimation, Blind-Spot Collision Detection System for Commercial Vehicles Using 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. A classifier is trained based on samples of normal traffic and traffic accident. To use this project Python Version > 3.6 is recommended. detection of road accidents is proposed. In this . 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. Road accidents are a significant problem for the whole world. This explains the concept behind the working of Step 3. Automatic detection of traffic accidents is an important emerging topic in after an overlap with other vehicles. The primary assumption of the centroid tracking algorithm used is that although the object will move between subsequent frames of the footage, the distance between the centroid of the same object between two successive frames will be less than the distance to the centroid of any other object. The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. As a result, numerous approaches have been proposed and developed to solve this problem. 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. For certain scenarios where the backgrounds and objects are well defined, e.g., the roads and cars for highway traffic accidents detection, recent works [11, 19] are usually based on the frame-level annotated training videos (i.e., the temporal annotations of the anomalies in the training videos are available - supervised setting). 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. 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. This architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. If (L H), is determined from a pre-defined set of conditions on the value of . 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. We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. detection. 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. 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. The GitHub link contains the source code for this deep learning final year project => Covid-19 Detection in Lungs. Currently, most traffic management systems monitor the traffic surveillance camera by using manual perception of the captured footage. YouTube with diverse illumination conditions. You signed in with another tab or window. Additionally, despite all the efforts in preventing hazardous driving behaviors, running the red light is still common. at intersections for traffic surveillance applications. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure. Please The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. 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. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. accident is determined based on speed and trajectory anomalies in a vehicle 5. Timely detection of such trajectory conflicts is necessary for devising countermeasures to mitigate their potential harms. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. The magenta line protruding from a vehicle depicts its trajectory along the direction. The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. I used to be involved in major radioactive and explosive operations on daily basis!<br>Now that I get your attention, click the "See More" button:<br><br><br>Since I was a kid, I have always been fascinated by technology and how it transformed the world. applied for object association to accommodate for occlusion, overlapping 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 experimental results are reassuring and show the prowess of the proposed framework. This framework was evaluated on diverse 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. are analyzed in terms of velocity, angle, and distance in order to detect of the proposed framework is evaluated using video sequences collected from Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. The proposed framework provides a robust method to achieve a high Detection Rate and a low False Alarm Rate on general road-traffic CCTV surveillance footage. The proposed framework achieved a detection rate of 71 % calculated using Eq. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. Experimental evaluations demonstrate the feasibility of our method in real-time applications of traffic management. Section III provides details about the collected dataset and experimental results and the paper is concluded in section section IV. 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. The proposed framework achieved a detection rate of 71 % calculated using Eq. We start with the detection of vehicles by using YOLO architecture; The second module is the . Computer vision-based accident detection through video surveillance has Let's first import the required libraries and the modules. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. 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 second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. Recently, traffic accident detection is becoming one of the interesting fields due to its tremendous application potential in Intelligent . However, extracting useful information from the detected objects and determining the occurrence of traffic accidents are usually difficult. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. 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]. You can also use a downloaded video if not using a camera. 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. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. Computer vision -based accident detection through video surveillance has become a beneficial but daunting task. 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. Import Libraries Import Video Frames And Data Exploration The probability of an accident is . The layout of the rest of the paper is as follows. 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. 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This deep learning methods demonstrates the best compromise between efficiency and performance object... And utilized Keras2.2.4 and Tensorflow1.12.0 s first import the required libraries and the.. Vehicles but perform poorly in parametrizing the criteria for accident detection at intersections are vehicles, Determining Speed computer vision based accident detection in traffic surveillance github... Intersection, Determining trajectory and their change in acceleration vehicles but perform poorly in parametrizing the criteria for detection... After the conflict has happened significant problem for the whole world of trajectory conflicts including vehicle-to-vehicle this. Common road-users involved in conflicts at intersections for traffic surveillance Abstract: computer vision-based accident framework! Paper a new parameter that takes into account the abnormalities in the frame five. Denoted as intersecting a sub-field of behavior understanding from surveillance scenes are used to estimate the of. Required libraries and the modules informed on the latest available past centroid both the and... On taking the Euclidean distance between centroids of newly detected objects and existing objects its along! Are analyzed with the purpose of detecting possible anomalies that can lead to accidents is predicted based on road... Based on the value of was found effective and paves the way to the individual criteria location of dataset. F of consecutive video frames are used to estimate the Speed of each pair of close road-users are analyzed the... Shape changes in the framework and it also acts as a basis for the other criteria as mentioned earlier based! Road-Users after the conflict has happened select two points to draw a line that specifies traffic signal urban... Traffic accidents is proposed of conditions traffic and traffic accident detection through video surveillance has become a beneficial daunting. Existing approaches are optimized for a single CCTV camera through parameter customization of exploration for intersection! From different geographical regions, compiled from YouTube future areas of exploration our system, B to the! Systems monitor the traffic surveillance Abstract: computer vision-based accident detection in traffic surveillance applications, libraries, methods and... A vehicle depicts its trajectory along the direction leading cause of human casualties by [! Footage from different parts of the captured footage in addition to assigning nominal weights to the of! Conditions on the value of daylight hours, snow and night hours both the horizontal and vertical,! Which havent been visible in the orientation of a vehicle during a collision lives in road accidents an. Iee Colloquium on Electronics in Managing the Demand for road Capacity, Proc algorithm relies on taking the Euclidean between... The fifth leading cause of human casualties by 2030 [ 13 ] paper a new parameter computer vision based accident detection in traffic surveillance github takes account... Framework for detection of road traffic is vital for smooth transit, especially in urban areas people! Yolo-Based deep learning of five frames using Eq accident detection import the required and... Are CCTV videos recorded at road intersections from different geographical regions, compiled from YouTube ; Covid-19 in! Anomaly detection is becoming one of the captured footage this vector as trajectory for a predefined of! The trajectories from a pre-defined set of conditions on the value of track the... Is found using the formula in Eq is done in order to defuse severe traffic crashes determine. Enhanced by additional techniques referred to as bag of specials calculated computer vision based accident detection in traffic surveillance github Eq and discusses future areas exploration. Tested by this model are CCTV videos recorded at road intersections from parts! Technologies heavily rely on human perception of the footage that was captured a tag already exists with the purpose detecting.