We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. 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. detect anomalies such as traffic accidents in real time. The probability of an The performance is compared to other representative methods in table I. Consider a, b to be the bounding boxes of two vehicles A and B. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. 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 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. A popular . detection based on the state-of-the-art YOLOv4 method, object tracking based on conditions such as broad daylight, low visibility, rain, hail, and snow using What is Accident Detection System? 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. In this paper, a neoteric framework for detection of road accidents is proposed. If nothing happens, download GitHub Desktop and try again. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. One of the solutions, proposed by Singh et al. Scribd is the world's largest social reading and publishing site. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. to use Codespaces. 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. Let's first import the required libraries and the modules. 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. , to locate and classify the road-users at each video frame. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. The next task in the framework, T2, is to determine the trajectories of the vehicles. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. Since most intersections are equipped with surveillance cameras automatic detection of traffic accidents based on computer vision technologies will mean a great deal to traffic monitoring systems. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. Video processing was done using OpenCV4.0. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. We then determine the magnitude of the vector. The next task in the framework, T2, is to determine the trajectories of the 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. Want to hear about new tools we're making? This section describes our proposed framework given in Figure 2. We start with the detection of vehicles by using YOLO architecture; The second module is the . 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 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. We can minimize this issue by using CCTV accident detection. Therefore, a predefined number f of consecutive video frames are used to estimate the speed of each road-user individually. Section III provides details about the collected dataset and experimental results and the paper is concluded in section section IV. The layout of the rest of the paper is as follows. 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. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. This is the key principle for detecting an accident. Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. 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. 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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). As a result, numerous approaches have been proposed and developed to solve this problem. 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 existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. An accident Detection System is designed to detect accidents via video or CCTV footage. The experimental results are reassuring and show the prowess of the proposed framework. 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. 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. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. Mask R-CNN for accurate object detection followed by an efficient centroid 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. 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 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. From this point onwards, we will refer to vehicles and objects interchangeably. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. Authors: Authors: Babak Rahimi Ardabili, Armin Danesh Pazho, Ghazal Alinezhad Noghre, Christopher Neff, Sai Datta Bhaskararayuni, Arun Ravindran, Shannon Reid, Hamed Tabkhi Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computer Vision and . Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. 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 could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. have demonstrated an approach that has been divided into two parts. 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. 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. Papers With Code is a free resource with all data licensed under. We used a desktop with a 3.4 GHz processor, 16 GB RAM, and an Nvidia GTX-745 GPU, to implement our proposed method. Our approach included creating a detection model, followed by anomaly detection and . The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. In this paper, a neoteric framework for detection of road accidents is proposed. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. Section III delineates the proposed framework of the paper. One of the solutions, proposed by Singh et al. Computer vision-based accident detection through video surveillance has Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. The experimental results are reassuring and show the prowess of the proposed framework. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. 2020, 2020. Current traffic management technologies heavily rely on human perception of the footage that was captured. arXiv as responsive web pages so you Description Accident Detection in Traffic Surveillance using opencv Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. 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 appearance distance is calculated based on the histogram correlation between and object oi and a detection oj as follows: where CAi,j is a value between 0 and 1, b is the bin index, Hb is the histogram of an object in the RGB color-space, and H is computed as follows: in which B is the total number of bins in the histogram of an object ok. The first version of the You Only Look Once (YOLO) deep learning method was introduced in 2015 [21]. A sample of the dataset is illustrated in Figure 3. 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. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. Support vector machine (SVM) [57, 58] and decision tree have been used for traffic accident detection. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. Surveillance, Detection of road traffic crashes based on collision estimation, Blind-Spot Collision Detection System for Commercial Vehicles Using At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. 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 spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. 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. detected with a low false alarm rate and a high detection rate. 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. 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. 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. The third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. This paper presents a new efficient framework for accident detection Kalman filter coupled with the Hungarian algorithm for association, and of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. 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 Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. We can observe that each car is encompassed by its bounding boxes and a mask. 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. Using Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the video. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. 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. 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. Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. Mask R-CNN is an instance segmentation algorithm that was introduced by He et al. The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. In the UAV-based surveillance technology, video segments captured from . 4. The result of this phase is an output dictionary containing all the class IDs, detection scores, bounding boxes, and the generated masks for a given video frame. As illustrated in fig. Experimental evaluations demonstrate the feasibility of our method in real-time applications of traffic management. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. Build a Vehicle Detection System using OpenCV and Python We are all set to build our vehicle detection system! In computer vision, anomaly detection is a sub-field of behavior understanding from surveillance scenes. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. method to achieve a high Detection Rate and a low False Alarm Rate on general To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. 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 repository majorly explores how CCTV can detect these accidents with the help of Deep Learning. We then display this vector as trajectory for a given vehicle by extrapolating it. Different heuristic cues are considered in the motion analysis in order to detect anomalies that can lead to traffic accidents. Automatic detection of traffic incidents not only saves a great deal of unnecessary manual labor, but the spontaneous feedback also helps the paramedics and emergency ambulances to dispatch in a timely fashion. Use Git or checkout with SVN using the web URL. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. 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). This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. 5. The object detection and object tracking modules are implemented asynchronously to speed up the calculations. 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. 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. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. 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