Numerous studies have applied computer vision techniques in traffic surveillance systems [26, 17, 9, 7, 6, 25, 8, 3, 10, 24] for various tasks. As in most image and video analytics systems the first step is to locate the objects of interest in the scene. We determine this parameter by determining the angle () of a vehicle with respect to its own trajectories over a course of an interval of five frames. 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. Note that if the locations of the bounding box centers among the f frames do not have a sizable change (more than a threshold), the object is considered to be slow-moving or stalled and is not involved in the speed calculations. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. In this paper, a neoteric framework for Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 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. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. at: http://github.com/hadi-ghnd/AccidentDetection. 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. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. 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. Consider a, b to be the bounding boxes of two vehicles A and B. The performance is compared to other representative methods in table I. The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. 1 holds true. surveillance cameras connected to traffic management systems. Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. The third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. 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]. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. 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. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. The object trajectories The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. 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. The proposed framework is purposely designed with efficient algorithms in order to be applicable in real-time traffic monitoring systems. Currently, most traffic management systems monitor the traffic surveillance camera by using manual perception of the captured footage. In the event of a collision, a circle encompasses the vehicles that collided is shown. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. [4]. The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. Anomalies are typically aberrations of scene entities (people, vehicles, environment) and their interactions from normal behavior. The robust tracking method accounts for challenging situations, such as occlusion, overlapping objects, and shape changes in tracking the objects of interest and recording their trajectories. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. This results in a 2D vector, representative of the direction of the vehicles motion. 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 parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. If you find a rendering bug, file an issue on GitHub. 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. We then normalize this vector by using scalar division of the obtained vector by its magnitude. 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 found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. applied for object association to accommodate for occlusion, overlapping The existing approaches are optimized for a single CCTV camera through parameter customization. PDF Abstract Code Edit No code implementations yet. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. Similarly, Hui et al. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. Video processing was done using OpenCV4.0. Experimental evaluations demonstrate the feasibility of our method in real-time applications of traffic management. This paper introduces a solution which uses state-of-the-art supervised deep learning framework. 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. A sample of the dataset is illustrated in Figure 3. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. Then, to run this python program, you need to execute the main.py python file. 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 incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. https://github.com/krishrustagi/Accident-Detection-System.git, To install all the packages required to run this python program 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. We determine the speed of the vehicle in a series of steps. The proposed framework consists of three hierarchical steps, including . Results, Statistics and Comparison with Existing models, F. Baselice, G. Ferraioli, G. Matuozzo, V. Pascazio, and G. Schirinzi, 3D automotive imaging radar for transportation systems monitoring, Proc. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. 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. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. The average processing speed is 35 frames per second (fps) which is feasible for real-time applications. 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 . , " A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition," Journal of advanced transportation, vol. In this paper, a new framework to detect vehicular collisions is proposed. 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. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. Section II succinctly debriefs related works and literature. The surveillance videos at 30 frames per second (FPS) are considered. 4. Additionally, the Kalman filter approach [13]. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. Use Git or checkout with SVN using the web URL. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. If the bounding boxes of the object pair overlap each other or are closer than a threshold the two objects are considered to be close. Considering the applicability of our method in real-time edge-computing systems, we apply the efficient and accurate YOLOv4 [2] method for object detection. In this paper, a neoteric framework for detection of road accidents is proposed. The trajectories are further analyzed to monitor the motion patterns of the detected road-users in terms of location, speed, and moving direction. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. In this . of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. The dataset includes day-time and night-time videos of various challenging weather and illumination conditions. Since here we are also interested in the category of the objects, we employ a state-of-the-art object detection method, namely YOLOv4 [2]. detection of road accidents is proposed. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. 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 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. If nothing happens, download GitHub Desktop and try again. 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. are analyzed in terms of velocity, angle, and distance in order to detect While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. 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. We can observe that each car is encompassed by its bounding boxes and a mask. The distance in kilometers can then be calculated by applying the haversine formula [4] as follows: where p and q are the latitudes, p and q are the longitudes of the first and second averaged points p and q, respectively, h is the haversine of the central angle between the two points, r6371 kilometers is the radius of earth, and dh(p,q) is the distance between the points p and q in real-world plane in kilometers. 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. Our framework is able to report the occurrence of trajectory conflicts along with the types of the road-users involved immediately. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. Mask R-CNN not only provides the advantages of Instance Segmentation but also improves the core accuracy by using RoI Align algorithm. Description Accident Detection in Traffic Surveillance using opencv Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. We then display this vector as trajectory for a given vehicle by extrapolating it. 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. Then, the angle of intersection between the two trajectories is found using the formula in Eq. This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. Timely detection of such trajectory conflicts is necessary for devising countermeasures to mitigate their potential harms. 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. 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. 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. 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). This explains the concept behind the working of Step 3. 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. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. One of the solutions, proposed by Singh et al. We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. Our approach included creating a detection model, followed by anomaly detection and . The existing approaches are optimized for a single CCTV camera through parameter customization. Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. 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. Therefore, computer vision techniques can be viable tools for automatic accident detection. Section III provides details about the collected dataset and experimental results and the paper is concluded in section section IV. This architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. Section III delineates the proposed framework of the paper. 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. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. 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. These object pairs can potentially engage in a conflict and they are therefore, chosen for further analysis. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. Sign up to our mailing list for occasional updates. Using Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the video. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. 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). Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. Typically, anomaly detection methods learn the normal behavior via training. Otherwise, we discard it. The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. 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. We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. 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. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. Even though their second part is a robust way of ensuring correct accident detections, their first part of the method faces severe challenges in accurate vehicular detections such as, in the case of environmental objects obstructing parts of the screen of the camera, or similar objects overlapping their shadows and so on. Or, have a go at fixing it yourself the renderer is open source! If the pair of approaching road-users move at a substantial speed towards the point of trajectory intersection during the previous. 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]. 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. 7. A dataset of various traffic videos containing accident or near-accident scenarios is collected to test the performance of the proposed framework against real videos. 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%. This framework was evaluated on diverse Support vector machine (SVM) [57, 58] and decision tree have been used for traffic accident detection. 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. YouTube with diverse illumination conditions. Moreover, Ki et al. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. 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. Import Libraries Import Video Frames And Data Exploration The second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. In the event of a collision, a circle encompasses the vehicles that collided is shown. 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. 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. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure 1. This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. 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. Section II succinctly debriefs related works and literature. Edit social preview. This results in a 2D vector, representative of the direction of the vehicles motion. Here we employ a simple but effective tracking strategy similar to that of the Simple Online and Realtime Tracking (SORT) approach [1]. We then determine the magnitude of the vector, , as shown in Eq. After that administrator will need to select two points to draw a line that specifies traffic signal. The velocity components are updated when a detection is associated to a target. Therefore, Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. 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. 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. Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. Papers With Code is a free resource with all data licensed under. Leaving abandoned objects on the road for long periods is dangerous, so . Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. 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. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. 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. From this point onwards, we will refer to vehicles and objects interchangeably. 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