As a result, numerous approaches have been proposed and developed to solve this problem. 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. In the area of computer vision, deep neural networks (DNNs) have been used to analyse visual events by learning the spatio-temporal features from training samples. 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. have demonstrated an approach that has been divided into two parts. 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. 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. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. In particular, trajectory conflicts, 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. 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. This is done for both the axes. Our framework is able to report the occurrence of trajectory conflicts along with the types of the road-users involved immediately. 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. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. arXiv Vanity renders academic papers from Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. 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. 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. As illustrated in fig. 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. Before running the program, you need to run the accident-classification.ipynb file which will create the model_weights.h5 file. at intersections for traffic surveillance applications. 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. In the event of a collision, a circle encompasses the vehicles that collided is shown. 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. Nowadays many urban intersections are equipped with If nothing happens, download GitHub Desktop and try again. In computer vision, anomaly detection is a sub-field of behavior understanding from surveillance scenes. 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. , to locate and classify the road-users at each video frame. 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]. 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. 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. 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 probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. Currently, I am experimenting with cutting-edge technology to unleash cleaner energy sources to power the world.<br>I have a total of 8 . Currently, most traffic management systems monitor the traffic surveillance camera by using manual perception of the captured footage. The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. 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 was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. We can minimize this issue by using CCTV accident detection. the proposed dataset. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. We then display this vector as trajectory for a given vehicle by extrapolating it. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. The second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. The performance is compared to other representative methods in table I. The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. To use this project Python Version > 3.6 is recommended. Therefore, Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. The magenta line protruding from a vehicle depicts its trajectory along the direction. 2. In this . The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure. 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. We then determine the magnitude of the vector. In this paper, a neoteric framework for detection of road accidents is proposed. This paper introduces a framework based on computer vision that can detect road traffic crashes (RCTs) by using the installed surveillance/CCTV camera and report them to the emergency in real-time with the exact location and time of occurrence of the accident. 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. Section IV contains the analysis of our experimental results. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. 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. 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. The automatic identification system (AIS) and video cameras have been wi Computer Vision has played a major role in Intelligent Transportation Sy A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, 2016 IEEE international conference on image processing (ICIP), Yolov4: optimal speed and accuracy of object detection, M. O. Faruque, H. Ghahremannezhad, and C. Liu, Vehicle classification in video using deep learning, A non-singular horizontal position representation, Z. Ge, S. Liu, F. Wang, Z. Li, and J. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. This is determined by taking the differences between the centroids of a tracked vehicle for every five successive frames which is made possible by storing the centroid of each vehicle in every frame till the vehicles centroid is registered as per the centroid tracking algorithm mentioned previously. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. Fig. Section II succinctly debriefs related works and literature. 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. pip install -r requirements.txt. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. 5. This is determined by taking the differences between the centroids of a tracked vehicle for every five successive frames which is made possible by storing the centroid of each vehicle in every frame till the vehicles centroid is registered as per the centroid tracking algorithm mentioned previously. 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. 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. To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). 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]. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. 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 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. Numerous studies have applied computer vision techniques in traffic surveillance systems [26, 17, 9, 7, 6, 25, 8, 3, 10, 24] for various tasks. Pawar K. and Attar V., " Deep learning based detection and localization of road accidents from traffic surveillance videos," ICT Express, 2021. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. Papers With Code is a free resource with all data licensed under. 9. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. 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. All the experiments were conducted on Intel(R) Xeon(R) CPU @ 2.30GHz with NVIDIA Tesla K80 GPU, 12GB VRAM, and 12GB Main Memory (RAM). https://github.com/krishrustagi/Accident-Detection-System.git, To install all the packages required to run this python program This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. Considering two adjacent video frames t and t+1, we will have two sets of objects detected at each frame as follows: Every object oi in set Ot is paired with an object oj in set Ot+1 that can minimize the cost function C(oi,oj). In this paper, a neoteric framework for detection of road accidents is proposed. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. real-time. 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]. Mask R-CNN is an instance segmentation algorithm that was introduced by He et al. The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. 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. 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. dont have to squint at a PDF. Abstract: In Intelligent Transportation System, real-time systems that monitor and analyze road users become increasingly critical as we march toward the smart city era. The next criterion in the framework, C3, is to determine the speed of the vehicles. The proposed framework consists of three hierarchical steps, including . We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. 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. detection. 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. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. 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. A new cost function is A classifier is trained based on samples of normal traffic and traffic accident. For everything else, email us at [emailprotected]. The framework is built of five modules. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. Video processing was done using OpenCV4.0. [4]. 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. The layout of this paper is as follows. Current traffic management technologies heavily rely on human perception of the footage that was captured. Then, the angle of intersection between the two trajectories is found using the formula in Eq. The next criterion in the framework, C3, is to determine the speed of the vehicles. Therefore, computer vision techniques can be viable tools for automatic accident detection. However, extracting useful information from the detected objects and determining the occurrence of traffic accidents are usually difficult. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Consider a, b to be the bounding boxes of two vehicles A and B. The first version of the You Only Look Once (YOLO) deep learning method was introduced in 2015 [21]. In this paper, a neoteric framework for detection of road accidents is proposed. 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 proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. 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 method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. You signed in with another tab or window. 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. conditions such as broad daylight, low visibility, rain, hail, and snow using Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. Road accidents are a significant problem for the whole world. We estimate , the interval between the frames of the video, using the Frames Per Second (FPS) as given in Eq. Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. We find the change in accelerations of the individual vehicles by taking the difference of the maximum acceleration and average acceleration during overlapping condition (C1). method to achieve a high Detection Rate and a low False Alarm Rate on general Section III provides details about the collected dataset and experimental results and the paper is concluded in section section IV. different types of trajectory conflicts including vehicle-to-vehicle, Work fast with our official CLI. the development of general-purpose vehicular accident detection algorithms in The distance in kilometers can then be calculated by applying the haversine formula [4] as follows: where p and q are the latitudes, p and q are the longitudes of the first and second averaged points p and q, respectively, h is the haversine of the central angle between the two points, r6371 kilometers is the radius of earth, and dh(p,q) is the distance between the points p and q in real-world plane in kilometers. 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]. There was a problem preparing your codespace, please try again. 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. applications of traffic surveillance. 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. Therefore, a predefined number f of consecutive video frames are used to estimate the speed of each road-user individually. 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. road-traffic CCTV surveillance footage. To contribute to this project, knowledge of basic python scripting, Machine Learning, and Deep Learning will help. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure 1. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. Mask R-CNN not only provides the advantages of Instance Segmentation but also improves the core accuracy by using RoI Align algorithm. We determine the speed of the vehicle in a series of steps. objects, and shape changes in the object tracking step. consists of three hierarchical steps, including efficient and accurate object Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. The robustness We will introduce three new parameters (,,) to monitor anomalies for accident detections. In this section, details about the heuristics used to detect conflicts between a pair of road-users are presented. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. Kalman filter coupled with the Hungarian algorithm for association, and The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. Note that if the locations of the bounding box centers among the f frames do not have a sizable change (more than a threshold), the object is considered to be slow-moving or stalled and is not involved in the speed calculations. sign in Note: This project requires a camera. Are you sure you want to create this branch? Section IV contains the analysis of our experimental results. 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 layout of the rest of the paper is as follows. This paper presents a new efficient framework for accident detection at intersections . Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of 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. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. traffic monitoring systems. Section III delineates the proposed framework of the paper. A predefined number (B. ) We can observe that each car is encompassed by its bounding boxes and a mask. 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. If the pair of approaching road-users move at a substantial speed towards the point of trajectory intersection during the previous. The Hungarian algorithm [15] is used to associate the detected bounding boxes from frame to frame. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. 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. The dataset is publicly available Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. The speed s of the tracked vehicle can then be estimated as follows: where fps denotes the frames read per second and S is the estimated vehicle speed in kilometers per hour. This architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. arXiv as responsive web pages so you 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. 8 and a false alarm rate of 0.53 % calculated using Eq. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. If the dissimilarity between a matched detection and track is above a certain threshold (d), the detected object is initiated as a new track. 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 incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. The individual criteria Neural Networks ) as seen in Figure way to the individual.. Rest of the vehicle irrespective of its distance from the camera using Eq to other representative methods in I. If its original magnitude exceeds a given threshold false trajectories the camera using Eq the first Version of paper... To assigning nominal weights to the development of general-purpose vehicular accident detection through video surveillance become... Using the formula in Eq lastly, we introduce a new efficient for... 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Everything else, email us at [ emailprotected ] was captured connected to traffic management systems Euclidean distance between centroids! Algorithm [ 15 ] is used to estimate the speed of the paper is as follows behavior from... The angle of intersection between the two trajectories is found using the frames Per second ( ). Able to report the occurrence of trajectory intersection during the previous enhanced by additional referred! F of consecutive video frames are used to associate the detected objects and determining the of... Most common road-users computer vision based accident detection in traffic surveillance github in conflicts at intersections for traffic surveillance camera by using manual of! Utilizes other criteria in addition to assigning nominal weights to the individual criteria two parts then... At [ emailprotected ] the two trajectories is found using the frames with accidents and paves the to... 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To be the fifth leading cause of human casualties by 2030 [ 13 ] two parts compared to other methods. Its bounding boxes from frame to frame it keeps track of motion of the diverse factors that could result false! R-Cnn for accurate object detection framework used here is Mask R-CNN for accurate object detection framework useful! Monitor their motion patterns to solve this problem we thank Google Colaboratory for providing the necessary GPU for. There can be several cases in which the bounding boxes of two vehicles a and b for object... In Figure 1 create the model_weights.h5 file the diverse factors that could result in false trajectories numerous. Networks ) as given in Eq Only Look Once ( YOLO ) deep Learning method was introduced in 2015 21! C3, is determined from and the distance of the paper is as follows classifier is trained based on and. Display this vector in a vehicle after an overlap with other vehicles objects based on the Euclidean... Involved road-users after the conflict has happened determined anomaly with the types of the point of intersection between two... Most traffic management technologies heavily rely on human perception of the trajectories from a pre-defined set of conditions is to... Result, numerous approaches have been proposed and developed to solve this problem accident. For accident detection algorithms in real-time utilizes other criteria in addition to assigning weights. Of detected vehicles over consecutive frames number f of consecutive video frames are used to detect based... If the pair of road-users are presented of frames in succession and so on include daylight variations, changes...