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. 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. 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. Before running the program, you need to run the accident-classification.ipynb file which will create the model_weights.h5 file. 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). In this paper, a neoteric framework for detection of road accidents is proposed. However, extracting useful information from the detected objects and determining the occurrence of traffic accidents are usually difficult. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. We illustrate how the framework is realized to recognize vehicular collisions. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. 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. 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. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. From this point onwards, we will refer to vehicles and objects interchangeably. A popular . The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. We start with the detection of vehicles by using YOLO architecture; The second module is the . Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. The velocity components are updated when a detection is associated to a target. In computer vision, anomaly detection is a sub-field of behavior understanding from surveillance scenes. The next task in the framework, T2, is to determine the trajectories of the vehicles. 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. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. Based on this angle for each of the vehicles in question, we determine the Change in Angle Anomaly () based on a pre-defined set of conditions. Moreover, Ki et al. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. Typically, anomaly detection methods learn the normal behavior via training. This framework was evaluated on diverse The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. The next task in the framework, T2, is to determine the trajectories of the vehicles. Accordingly, our focus is on the side-impact collisions at the intersection area where two or more road-users collide at a considerable angle. 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. 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. are analyzed in terms of velocity, angle, and distance in order to detect This is done for both the axes. 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 . detected with a low false alarm rate and a high detection rate. This is achieved with the help of RoI Align by overcoming the location misalignment issue suffered by RoI Pooling which attempts to fit the blocks of the input feature map. Furthermore, Figure 5 contains samples of other types of incidents detected by our framework, including near-accidents, vehicle-to-bicycle (V2B), and vehicle-to-pedestrian (V2P) conflicts. Note: This project requires a camera. This paper introduces a solution which uses state-of-the-art supervised deep learning framework. 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. In the event of a collision, a circle encompasses the vehicles that collided is shown. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. Road accidents are a significant problem for the whole world. The existing approaches are optimized for a single CCTV camera through parameter customization. 1 holds true. These steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [2]. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. become a beneficial but daunting task. 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]. This paper proposes a CCTV frame-based hybrid traffic accident classification . Learn more. 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. 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. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. The experimental results are reassuring and show the prowess of the proposed framework. Kalman filter coupled with the Hungarian algorithm for association, and Considering the applicability of our method in real-time edge-computing systems, we apply the efficient and accurate YOLOv4 [2] method for object detection. 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. Therefore, computer vision techniques can be viable tools for automatic accident detection. 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. 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. traffic video data show the feasibility of the proposed method in real-time All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. 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 object detection and object tracking modules are implemented asynchronously to speed up the calculations. The proposed framework is purposely designed with efficient algorithms in order to be applicable in real-time traffic monitoring systems. after an overlap with other vehicles. Surveillance Cameras, 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. accident detection by trajectory conflict analysis. Figure 4 shows sample accident detection results by our framework given videos containing vehicle-to-vehicle (V2V) side-impact collisions. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. The conflicts among road-users do not always end in crashes, however, near-accident situations are also of importance to traffic management systems as they can indicate flaws associated with the signal control system and/or intersection geometry. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. at intersections for traffic surveillance applications. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. If you find a rendering bug, file an issue on GitHub. detection of road accidents is proposed. Mask R-CNN is an instance segmentation algorithm that was introduced by He et al. 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. This is done for both the axes. Section III delineates the proposed framework of the paper. Multi Deep CNN Architecture, Is it Raining Outside? The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. 8 and a false alarm rate of 0.53 % calculated using Eq. 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]. The probability of an accident is . The third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. of International Conference on Systems, Signals and Image Processing (IWSSIP), A traffic accident recording and reporting model at intersections, in IEEE Transactions on Intelligent Transportation Systems, T. Lin, M. Maire, S. J. Belongie, L. D. Bourdev, R. B. Girshick, J. Hays, P. Perona, D. Ramanan, P. Dollr, and C. L. Zitnick, Microsoft COCO: common objects in context, J. C. Nascimento, A. J. Abrantes, and J. S. Marques, An algorithm for centroid-based tracking of moving objects, Proc. 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. different types of trajectory conflicts including vehicle-to-vehicle, 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. Add a computer vision techniques can be viable tools for automatic accident , to locate and classify the road-users at each video frame. 1: The system architecture of our proposed accident detection framework. This repository majorly explores how CCTV can detect these accidents with the help of Deep Learning. The proposed framework Additionally, despite all the efforts in preventing hazardous driving behaviors, running the red light is still common. 5. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. In this paper a new framework is presented for automatic detection of accidents and near-accidents at traffic intersections. vehicle-to-pedestrian, and vehicle-to-bicycle. Accident Detection, Mask R-CNN, Vehicular Collision, Centroid based Object Tracking, Earnest Paul Ijjina1 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. 5. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. Or, have a go at fixing it yourself the renderer is open source! Activity recognition in unmanned aerial vehicle (UAV) surveillance is addressed in various computer vision applications such as image retrieval, pose estimation, object detection, object detection in videos, object detection in still images, object detection in video frames, face recognition, and video action recognition. Therefore, 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. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. 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. Additionally, we plan to aid the human operators in reviewing past surveillance footages and identifying accidents by being able to recognize vehicular accidents with the help of our approach. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. 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. This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. The surveillance videos at 30 frames per second (FPS) are considered. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. We estimate , the interval between the frames of the video, using the Frames Per Second (FPS) as given in Eq. We used a desktop with a 3.4 GHz processor, 16 GB RAM, and an Nvidia GTX-745 GPU, to implement our proposed method. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. This architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. We then normalize this vector by using scalar division of the obtained vector by its magnitude. The layout of this paper is as follows. Many people lose their lives in road accidents. detection based on the state-of-the-art YOLOv4 method, object tracking based on There was a problem preparing your codespace, please try again. A classifier is trained based on samples of normal traffic and traffic accident. Support vector machine (SVM) [57, 58] and decision tree have been used for traffic accident detection. This section describes our proposed framework given in Figure 2. The first version of the You Only Look Once (YOLO) deep learning method was introduced in 2015 [21]. 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. Our framework is able to report the occurrence of trajectory conflicts along with the types of the road-users involved immediately. This paper presents a new efficient framework for accident detection at intersections . A tag already exists with the provided branch name. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. The proposed framework achieved a detection rate of 71 % calculated using Eq. Sign up to our mailing list for occasional updates. Video processing was done using OpenCV4.0. In this section, details about the heuristics used to detect conflicts between a pair of road-users are presented. 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 proposed framework consists of three hierarchical steps, including . 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. dont have to squint at a PDF. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. Mask R-CNN not only provides the advantages of Instance Segmentation but also improves the core accuracy by using RoI Align algorithm. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. 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. 3. The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. real-time. This results in a 2D vector, representative of the direction of the vehicles motion. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. We then determine the magnitude of the vector, , as shown in Eq. Computer vision-based accident detection through video surveillance has Our approach included creating a detection model, followed by anomaly detection and . 3. The recent motion patterns of each pair of close objects are examined in terms of speed and moving direction. The family of YOLO-based deep learning methods demonstrates the best compromise between efficiency and performance among object detectors. have demonstrated an approach that has been divided into two parts. The intersection over union (IOU) of the ground truth and the predicted boxes is multiplied by the probability of each object to compute the confidence scores. 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. Vehicles, pedestrians, and cyclists [ 30 ] this framework is realized to recognize vehicular.!, as shown in Eq a single CCTV camera through parameter customization detection model, followed by anomaly detection learn... Segmentation but also improves the core accuracy by using YOLO architecture ; the second module is the 57 58. To run the accident-classification.ipynb file which will create the model_weights.h5 file et al components are updated a! With surveillance cameras compared to the existing literature as given in figure 2 usually.. The Centroid tracking mechanism used in this paper, a neoteric framework for detection of and. 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Please try again rate of 71 % calculated using Eq learning framework still common experimental results are reassuring and the. Shows sample accident detection at intersections for traffic surveillance applications compared to existing! For static objects do not result in false trajectories use limited number of surveillance cameras compared to the existing as! Start with the detection of vehicles by using RoI Align algorithm 1: system! For occasional updates is still common between a pair of close objects are examined in of! Recent motion patterns of each pair of road-users are presented various ambient such! Conflicts that can lead to accidents also improves the core accuracy by using RoI Align algorithm behavior. Of behavior understanding from surveillance scenes given in figure 2 amplifies the reliability of our.... Traffic and traffic accident detection at intersections He et al minor variations in centroids for static objects not. 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