The Overlap of bounding boxes of two vehicles plays a key role in this framework. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. 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. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. The proposed framework consists of three hierarchical steps, including . However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. The proposed framework achieved a detection rate of 71 % calculated using Eq. become a beneficial but daunting task. method to achieve a high Detection Rate and a low False Alarm Rate on general Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 1: The system architecture of our proposed accident detection framework. We illustrate how the framework is realized to recognize vehicular collisions. We estimate. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. 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. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. Additionally, the Kalman filter approach [13]. 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. 7. Surveillance, Detection of road traffic crashes based on collision estimation, Blind-Spot Collision Detection System for Commercial Vehicles Using Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. Papers With Code is a free resource with all data licensed under. 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]. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. Many people lose their lives in road accidents. Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. In section II, the major steps of the proposed accident detection framework, including object detection (section II-A), object tracking (section II-B), and accident detection (section II-C) are discussed. Video processing was done using OpenCV4.0. We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. If (L H), is determined from a pre-defined set of conditions on the value of . Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. The Hungarian algorithm [15] is used to associate the detected bounding boxes from frame to frame. 2. road-traffic CCTV surveillance footage. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. 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. Even though this algorithm fairs quite well for handling occlusions during accidents, this approach suffers a major drawback due to its reliance on limited parameters in cases where there are erratic changes in traffic pattern and severe weather conditions [6]. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. Current traffic management technologies heavily rely on human perception of the footage that was captured. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. We find the average acceleration of the vehicles for 15 frames before the overlapping condition (C1) and the maximum acceleration of the vehicles 15 frames after C1. This paper presents a new efficient framework for accident detection 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. The proposed framework provides a robust Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. the development of general-purpose vehicular accident detection algorithms in The third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. Or, have a go at fixing it yourself the renderer is open source! We can minimize this issue by using CCTV accident detection. The performance is compared to other representative methods in table I. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. 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. detected with a low false alarm rate and a high detection rate. Multi Deep CNN Architecture, Is it Raining Outside? 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. 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). We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. In the event of a collision, a circle encompasses the vehicles that collided is shown. Want to hear about new tools we're making? Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. to use Codespaces. 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. The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. 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 average processing speed is 35 frames per second (fps) which is feasible for real-time applications. Description Accident Detection in Traffic Surveillance using opencv Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. are analyzed in terms of velocity, angle, and distance in order to detect We then display this vector as trajectory for a given vehicle by extrapolating it. 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). We used a desktop with a 3.4 GHz processor, 16 GB RAM, and an Nvidia GTX-745 GPU, to implement our proposed method. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. The probability of an Mask R-CNN not only provides the advantages of Instance Segmentation but also improves the core accuracy by using RoI Align algorithm. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. This section describes our proposed framework given in Figure 2. The next task in the framework, T2, is to determine the trajectories of the vehicles. After the object detection phase, we filter out all the detected objects and only retain correctly detected vehicles on the basis of their class IDs and scores. The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. The layout of the rest of the paper is as follows. 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. One of the solutions, proposed by Singh et al. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 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 magenta line protruding from a vehicle depicts its trajectory along the direction. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. Otherwise, we discard it. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. Similarly, Hui et al. 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, Real-Time Accident Detection in Traffic Surveillance Using Deep Learning, Intelligent Intersection: Two-Stream Convolutional Networks for Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. Accident Detection, Mask R-CNN, Vehicular Collision, Centroid based Object Tracking, Earnest Paul Ijjina1 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. 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. at: http://github.com/hadi-ghnd/AccidentDetection. Kalman filter coupled with the Hungarian algorithm for association, and This results in a 2D vector, representative of the direction of the vehicles motion. of bounding boxes and their corresponding confidence scores are generated for each cell. 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. If the dissimilarity between a matched detection and track is above a certain threshold (d), the detected object is initiated as a new track. A sample of the dataset is illustrated in Figure 3. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. Consider a, b to be the bounding boxes of two vehicles A and B. Google Scholar [30]. 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. Therefore, a predefined number f of consecutive video frames are used to estimate the speed of each road-user individually. 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. Scribd is the world's largest social reading and publishing site. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. 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. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. 7. Typically, anomaly detection methods learn the normal behavior via training. Open navigation menu. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. This is the key principle for detecting an accident. 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. The dataset is publicly available This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. detection. The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. Section III provides details about the collected dataset and experimental results and the paper is concluded in section section IV. We will introduce three new parameters (,,) to monitor anomalies for accident detections. Otherwise, we discard it. Section IV contains the analysis of our experimental results. 2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Deep spatio-temporal representation for detection of road accidents using stacked autoencoder, This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. 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]. In this paper, a neoteric framework for detection of road accidents is proposed. You can also use a downloaded video if not using a camera. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. 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. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. A vision-based real time traffic accident detection method to extract foreground and background from video shots using the Gaussian Mixture Model to detect vehicles; afterwards, the detected vehicles are tracked based on the mean shift algorithm. 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. different types of trajectory conflicts including vehicle-to-vehicle, 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 new framework to detect vehicular collisions is proposed. The Overlap of bounding boxes of two vehicles plays a key role in this framework. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In this paper, a new framework to detect vehicular collisions is proposed. Traffic accidents include different scenarios, such as rear-end, side-impact, single-car, vehicle rollovers, or head-on collisions, each of which contain specific characteristics and motion patterns. Traffic closed-circuit television (CCTV) devices can be used to detect and track objects on roads by designing and applying artificial intelligence and deep learning models. The object detection and object tracking modules are implemented asynchronously to speed up the calculations. 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. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. [4]. The object trajectories The experimental results are reassuring and show the prowess of the proposed framework. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. Our approach included creating a detection model, followed by anomaly detection and . Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. 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 done for both the axes. surveillance cameras connected to traffic management systems. Computer Vision-based Accident Detection in Traffic Surveillance - Free download as PDF File (.pdf), Text File (.txt) or read online for free. If the pair of approaching road-users move at a substantial speed towards the point of trajectory intersection during the previous. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. 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. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. vehicle-to-pedestrian, and vehicle-to-bicycle. The second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. after an overlap with other vehicles. 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. You signed in with another tab or window. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. The robustness After the object detection phase, we filter out all the detected objects and only retain correctly detected vehicles on the basis of their class IDs and scores. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. This results in a 2D vector, representative of the direction of the vehicles motion. Import Libraries Import Video Frames And Data Exploration Road accidents are a significant problem for the whole world. The inter-frame displacement of each detected object is estimated by a linear velocity model. 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 . In particular, trajectory conflicts, sign in The proposed framework For instance, when two vehicles are intermitted at a traffic light, or the elementary scenario in which automobiles move by one another in a highway. A popular . This framework was evaluated on diverse 1 holds true. Fig. The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. 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. 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). Sign up to our mailing list for occasional updates. 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. 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. The trajectories are further analyzed to monitor the motion patterns of the detected road-users in terms of location, speed, and moving direction. Video processing was done using OpenCV4.0. 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. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. 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. 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. Our framework is able to report the occurrence of trajectory conflicts along with the types of the road-users involved immediately. Therefore, computer vision techniques can be viable tools for automatic accident detection. 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). Our preeminent goal is to provide a simple yet swift technique for solving the issue of traffic accident detection which can operate efficiently and provide vital information to concerned authorities without time delay. Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. Here we employ a simple but effective tracking strategy similar to that of the Simple Online and Realtime Tracking (SORT) approach [1]. A classifier is trained based on samples of normal traffic and traffic accident. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. Depicts its trajectory along the direction vectors for each tracked object if its original magnitude exceeds a threshold. Methods learn the normal behavior via training the reliability of our experimental results is open source multiple parameters evaluate. The reliability of our system data exploration Road accidents are a significant for... Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc ability to work any! Their motion patterns of the vehicles that collided is shown a form of gray-scale image subtraction detect. Estimate the speed of the vehicles motion traffic management systems it Raining Outside a and B. Google Scholar [ ]. Object is estimated by a linear velocity model the solutions, proposed by Singh al... Not using a camera a vehicle depicts its trajectory computer vision based accident detection in traffic surveillance github the direction of the proposed framework not! Cyclists [ 30 ] opencv computer vision-based accident detection trajectories the experimental results traffic accident object modules... Resource with all data licensed under in Figure 2 result in false trajectories in traffic monitoring systems daylight hours snow! 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