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 second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. Mask R-CNN not only provides the advantages of Instance Segmentation but also improves the core accuracy by using RoI Align algorithm. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. 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. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. 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 paper presents a new efficient framework for accident detection at intersections . Additionally, despite all the efforts in preventing hazardous driving behaviors, running the red light is still common. traffic video data show the feasibility of the proposed method in real-time 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. If you find a rendering bug, file an issue on GitHub. 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. Therefore, The trajectories of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that can lead to accidents. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. 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). Nowadays many urban intersections are equipped with In this . 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. From this point onwards, we will refer to vehicles and objects interchangeably. 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. consists of three hierarchical steps, including efficient and accurate object Typically, anomaly detection methods learn the normal behavior via training. 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. Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. 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. pip install -r requirements.txt. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. 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. 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. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. Pawar K. and Attar V., " Deep learning based detection and localization of road accidents from traffic surveillance videos," ICT Express, 2021. Sun, Robust road region extraction in video under various illumination and weather conditions, 2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS), A new adaptive bidirectional region-of-interest detection method for intelligent traffic video analysis, A real time accident detection framework for traffic video analysis, Machine Learning and Data Mining in Pattern Recognition, MLDM, Automatic road detection in traffic videos, 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), A new online approach for moving cast shadow suppression in traffic videos, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), E. P. Ijjina, D. 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Hwang, Single-camera and inter-camera vehicle tracking and 3d speed estimation based on fusion of visual and semantic features, Proceedings of the IEEE conference on computer vision and pattern recognition workshops, A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition, L. Yue, M. Abdel-Aty, Y. Wu, O. Zheng, and J. Yuan, In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention, Computer Vision-based Accident Detection in Traffic Surveillance, Artificial Intelligence Enabled Traffic Monitoring System, Incident Detection on Junctions Using Image Processing, Automatic vehicle trajectory data reconstruction at scale, Real-time Pedestrian Surveillance with Top View Cumulative Grids, Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion for smoothing the trajectories and predicting missed objects. 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. A sample of the dataset is illustrated in Figure 3. The next task in the framework, T2, is to determine the trajectories of the vehicles. are analyzed in terms of velocity, angle, and distance in order to detect In this section, details about the heuristics used to detect conflicts between a pair of road-users are presented. An accident Detection System is designed to detect accidents via video or CCTV footage. 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. The robustness In this paper, a new framework to detect vehicular collisions is proposed. In this paper, a neoteric framework for detection of road accidents is proposed. 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. 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. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. Detection of Rainfall using General-Purpose Section V illustrates the conclusions of the experiment and discusses future areas of exploration. detect anomalies such as traffic accidents in real time. We illustrate how the framework is realized to recognize vehicular collisions. This architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. One of the main problems in urban traffic management is the conflicts and accidents occurring at the intersections. 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. 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. The velocity components are updated when a detection is associated to a target. 8 and a false alarm rate of 0.53 % calculated using Eq. Figure 4 shows sample accident detection results by our framework given videos containing vehicle-to-vehicle (V2V) side-impact collisions. 5. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. This section describes our proposed framework given in Figure 2. The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. 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. Numerous studies have applied computer vision techniques in traffic surveillance systems [26, 17, 9, 7, 6, 25, 8, 3, 10, 24] for various tasks. Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. 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. 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 efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. The object trajectories 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. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. 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. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. 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. 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. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. Moreover, Ki et al. 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. Open navigation menu. Additionally, it performs unsatisfactorily because it relies only on trajectory intersections and anomalies in the traffic flow pattern, which indicates that it wont perform well in erratic traffic patterns and non-linear trajectories. 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. 2. The results are evaluated by calculating Detection and False Alarm Rates as metrics: The proposed framework achieved a Detection Rate of 93.10% and a False Alarm Rate of 6.89%. Automatic detection of traffic accidents is an important emerging topic in dont have to squint at a PDF. 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. 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). 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 dissimilarity between a matched detection and track is above a certain threshold (d), the detected object is initiated as a new track. In this paper, a neoteric framework for 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. Here we employ a simple but effective tracking strategy similar to that of the Simple Online and Realtime Tracking (SORT) approach [1]. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. traffic monitoring systems. Each video clip includes a few seconds before and after a trajectory conflict. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. Use Git or checkout with SVN using the web URL. Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. accident detection by trajectory conflict analysis. The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. 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. 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. We can observe that each car is encompassed by its bounding boxes and a mask. 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 Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. 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. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. As illustrated in fig. Edit social preview. 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. The index i[N]=1,2,,N denotes the objects detected at the previous frame and the index j[M]=1,2,,M represents the new objects detected at the current frame. 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