computer vision based accident detection in traffic surveillance githubcomputer vision based accident detection in traffic surveillance github
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. 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. The object detection and object tracking modules are implemented asynchronously to speed up the calculations. real-time. The magenta line protruding from a vehicle depicts its trajectory along the direction. Pawar K. and Attar V., " Deep learning based detection and localization of road accidents from traffic surveillance videos," ICT Express, 2021. 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. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. An accident Detection System is designed to detect accidents via video or CCTV footage. The proposed framework achieved a detection rate of 71 % calculated using Eq. conditions such as broad daylight, low visibility, rain, hail, and snow using A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. The two averaged points p and q are transformed to the real-world coordinates using the inverse of the homography matrix H1, which is calculated during camera calibration [28] by selecting a number of points on the frame and their corresponding locations on the Google Maps [11]. Otherwise, we discard it. 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. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. The next task in the framework, T2, is to determine the trajectories of the vehicles. In the UAV-based surveillance technology, video segments captured from . A sample of the dataset is illustrated in Figure 3. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. This section describes our proposed framework given in Figure 2. The average processing speed is 35 frames per second (fps) which is feasible for real-time applications. The probability of an accident is . The trajectories are further analyzed to monitor the motion patterns of the detected road-users in terms of location, speed, and moving direction. 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 Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. Video processing was done using OpenCV4.0. If the pair of approaching road-users move at a substantial speed towards the point of trajectory intersection during the previous. 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. 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 use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. A predefined number (B. ) This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. 8 and a false alarm rate of 0.53 % calculated using Eq. 5. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. 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. . 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. Multi Deep CNN Architecture, Is it Raining Outside? We estimate , the interval between the frames of the video, using the Frames Per Second (FPS) as given in Eq. Experimental results using real 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. We then normalize this vector by using scalar division of the obtained vector by its magnitude. In recent times, vehicular accident detection has become a prevalent field for utilizing computer vision [5], to overcome this arduous task of providing first-aid services on time without the need of a human operator for monitoring such event. Computer vision-based accident detection through video surveillance has 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. 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. 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 spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. 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 family of YOLO-based deep learning methods demonstrates the best compromise between efficiency and performance among object detectors. are analyzed in terms of velocity, angle, and distance in order to detect 5. 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). The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. 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 . Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. A classifier is trained based on samples of normal traffic and traffic accident. 2. You signed in with another tab or window. Selecting the region of interest will start violation detection system. The GitHub link contains the source code for this deep learning final year project => Covid-19 Detection in Lungs. Many people lose their lives in road accidents. Surveillance, Detection of road traffic crashes based on collision estimation, Blind-Spot Collision Detection System for Commercial Vehicles Using Our approach included creating a detection model, followed by anomaly detection and . Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. objects, and shape changes in the object tracking step. This architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. pip install -r requirements.txt. 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. 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. 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. 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). Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. We estimate. 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 . task. Numerous studies have applied computer vision techniques in traffic surveillance systems [26, 17, 9, 7, 6, 25, 8, 3, 10, 24] for various tasks. 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. 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]. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. An accident Detection System is designed to detect accidents via video or CCTV footage. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. 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 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. 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]. Real-time Near Accident Detection in Traffic Video, COLLIDE-PRED: Prediction of On-Road Collision From Surveillance Videos, Deep4Air: A Novel Deep Learning Framework for Airport Airside This results in a 2D vector, representative of the direction of the vehicles motion. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. The proposed framework achieved a detection rate of 71 % calculated using Eq. The trajectories of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that can lead to accidents. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. In this paper a new framework is presented for automatic detection of accidents and near-accidents at traffic intersections. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. 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. 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. Use Git or checkout with SVN using the web URL. The robustness 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. This is the key principle for detecting an accident. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. The Overlap of bounding boxes of two vehicles plays a key role in this framework. the development of general-purpose vehicular accident detection algorithms in Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. The Hungarian algorithm [15] is used to associate the detected bounding boxes from frame to frame. 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. 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. For everything else, email us at [emailprotected]. Section III provides details about the collected dataset and experimental results and the paper is concluded in section section IV. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. Sign up to our mailing list for occasional updates. 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. Mask R-CNN not only provides the advantages of Instance Segmentation but also improves the core accuracy by using RoI Align algorithm. Computer Vision-based Accident Detection in Traffic Surveillance Abstract: Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Considering the applicability of our method in real-time edge-computing systems, we apply the efficient and accurate YOLOv4 [2] method for object detection. 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. Description Accident Detection in Traffic Surveillance using opencv Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 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. This takes a substantial amount of effort from the point of view of the human operators and does not support any real-time feedback to spontaneous events. The next criterion in the framework, C3, is to determine the speed of the vehicles. 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. 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. Before running the program, you need to run the accident-classification.ipynb file which will create the model_weights.h5 file. The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. Mask R-CNN is an instance segmentation algorithm that was introduced by He et al. 1 holds true. 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 recent times, vehicular accident detection has become a prevalent field for utilizing computer vision [5] to overcome this arduous task of providing first-aid services on time without the need of a human operator for monitoring such event. Road accidents are a significant problem for the whole world. 3. Otherwise, we discard it. This paper introduces a solution which uses state-of-the-art supervised deep learning framework. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. 3. Section II succinctly debriefs related works and literature. 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. 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. Scribd is the world's largest social reading and publishing site. The proposed framework To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). We then display this vector as trajectory for a given vehicle by extrapolating it. An accident Detection System is designed to detect accidents via video or CCTV footage. 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. If (L H), is determined from a pre-defined set of conditions on the value of . 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. Typically, anomaly detection methods learn the normal behavior via training. Local features such as trajectory for a given vehicle by extrapolating it pedestrians, and shape changes in the utilizes! 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