We then display this vector as trajectory for a given vehicle by extrapolating it. We start with the detection of vehicles by using YOLO architecture; The second module is the . of the proposed framework is evaluated using video sequences collected from 1 holds true. The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. As illustrated in fig. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. 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. computer vision techniques can be viable tools for automatic accident 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. different types of trajectory conflicts including vehicle-to-vehicle, Accident Detection, Mask R-CNN, Vehicular Collision, Centroid based Object Tracking, Earnest Paul Ijjina1 https://github.com/krishrustagi/Accident-Detection-System.git, To install all the packages required to run this python program However, extracting useful information from the detected objects and determining the occurrence of traffic accidents are usually difficult. 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. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. These object pairs can potentially engage in a conflict and they are therefore, chosen for further analysis. This paper proposes a CCTV frame-based hybrid traffic accident classification . We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. The next task in the framework, T2, is to determine the trajectories of the vehicles. This section describes our proposed framework given in Figure 2. arXiv as responsive web pages so you An accident Detection System is designed to detect accidents via video or CCTV footage. Mask R-CNN for accurate object detection followed by an efficient centroid From this point onwards, we will refer to vehicles and objects interchangeably. 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. accident is determined based on speed and trajectory anomalies in a vehicle The average bounding box centers associated to each track at the first half and second half of the f frames are computed. Recently, traffic accident detection is becoming one of the interesting fields due to its tremendous application potential in Intelligent . The proposed framework provides a robust Using Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the video. 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. detection. 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 . Different heuristic cues are considered in the motion analysis in order to detect anomalies that can lead to traffic accidents. The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. The first version of the You Only Look Once (YOLO) deep learning method was introduced in 2015 [21]. Many people lose their lives in road accidents. 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. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). 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]. 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. 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. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. This section describes our proposed framework given in Figure 2. Section III delineates the proposed framework of the paper. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. Timely detection of such trajectory conflicts is necessary for devising countermeasures to mitigate their potential harms. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure 1. pip install -r requirements.txt. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. This is the key principle for detecting an accident. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. The next criterion in the framework, C3, is to determine the speed of the vehicles. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. 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 parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. If (L H), is determined from a pre-defined set of conditions on the value of . After that administrator will need to select two points to draw a line that specifies traffic signal. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. 8 and a false alarm rate of 0.53 % calculated using Eq. The existing approaches are optimized for a single CCTV camera through parameter customization. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. 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 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. for smoothing the trajectories and predicting missed objects. 2. We then normalize this vector by using scalar division of the obtained vector by its magnitude. We can use an alarm system that can call the nearest police station in case of an accident and also alert them of the severity of the accident. 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. 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. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. arXiv Vanity renders academic papers from 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. We then determine the magnitude of the vector, , as shown in Eq. The proposed framework Section IV contains the analysis of our experimental results. 3. Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. The trajectories of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that can lead to accidents. 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. The dataset is publicly available 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 minimize this issue by using CCTV accident detection. The proposed framework consists of three hierarchical steps, including . 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. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. 2020, 2020. You can also use a downloaded video if not using a camera. 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. detection of road accidents is proposed. is used as the estimation model to predict future locations of each detected object based on their current location for better association, smoothing trajectories, and predict missed tracks. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. Each video clip includes a few seconds before and after a trajectory conflict. Or, have a go at fixing it yourself the renderer is open source! 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. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. Multi Deep CNN Architecture, Is it Raining Outside? Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. This results in a 2D vector, representative of the direction of the vehicles motion. 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. Section II succinctly debriefs related works and literature. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. 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. 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. The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. Support vector machine (SVM) [57, 58] and decision tree have been used for traffic accident detection. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Use Git or checkout with SVN using the web URL. In this paper, a new framework to detect vehicular collisions is proposed. We then display this vector as trajectory for a given vehicle by extrapolating it. The inter-frame displacement of each detected object is estimated by a linear velocity model. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. 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. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. Detection of Rainfall using General-Purpose This explains the concept behind the working of Step 3. The total cost function is used by the Hungarian algorithm [15] to assign the detected objects at the current frame to the existing tracks. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: 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. We estimate , the interval between the frames of the video, using the Frames Per Second (FPS) as given in Eq. The second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. 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. 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. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. I used to be involved in major radioactive and explosive operations on daily basis!<br>Now that I get your attention, click the "See More" button:<br><br><br>Since I was a kid, I have always been fascinated by technology and how it transformed the world. The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. Work fast with our official CLI. Then, to run this python program, you need to execute the main.py python file. 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. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. As in most image and video analytics systems the first step is to locate the objects of interest in the scene. 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. 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. The model of computer-assisted analysis of lung ultrasound image is built which has shown great potential in pulmonary condition diagnosis and is also used as an alternative for diagnosis of COVID-19 in a patient. The magenta line protruding from a vehicle depicts its trajectory along the direction. 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. 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. Mask R-CNN not only provides the advantages of Instance Segmentation but also improves the core accuracy by using RoI Align algorithm. Papers With Code is a free resource with all data licensed under. Consider a, b to be the bounding boxes of two vehicles A and B. 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. Open navigation menu. 8 and a false alarm rate of 0.53 % calculated using Eq. 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 overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The magenta line protruding from a vehicle depicts its trajectory along the direction. Section II succinctly debriefs related works and literature. 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. 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. 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. A sample of the dataset is illustrated in Figure 3. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. Computer Vision-based Accident Detection in Traffic Surveillance - Free download as PDF File (.pdf), Text File (.txt) or read online for free. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. Otherwise, we discard it. If you find a rendering bug, file an issue on GitHub. The state of each target in the Kalman filter tracking approach is presented as follows: where xi and yi represent the horizontal and vertical locations of the bounding box center, si, and ri represent the bounding box scale and aspect ratio, and xi,yi,si are the velocities in each parameter xi,yi,si of object oi at frame t, respectively. A tag already exists with the provided branch name. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. Add a Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. This results in a 2D vector, representative of the direction of the vehicles motion. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. In this . Let x, y be the coordinates of the centroid of a given vehicle and let , be the width and height of the bounding box of a vehicle respectively. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. If nothing happens, download GitHub Desktop and try again. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. We can observe that each car is encompassed by its bounding boxes and a mask. All the experiments conducted in relation to this framework validate the potency and efficiency of the proposition and thereby authenticates the fact that the framework can render timely, valuable information to the concerned authorities. If (L H), is determined from a pre-defined set of conditions on the value of . 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 framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. 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 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. 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). . Typically, anomaly detection methods learn the normal behavior via training. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. 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. Video processing was done using OpenCV4.0. 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 velocity components are updated when a detection is associated to a target. 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). of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. We can observe that each car is encompassed by its bounding boxes and a mask. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. 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. 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. In the area of computer vision, deep neural networks (DNNs) have been used to analyse visual events by learning the spatio-temporal features from training samples. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. In most image and video analytics systems the first part takes the input and a... Assigning nominal weights to the development of general-purpose vehicular accident detection is becoming of. Is to determine the magnitude of the vehicles new efficient framework for accident detection is becoming one the! Necessarily lead to accidents the shortest Euclidean distance between centroids of detected vehicles over consecutive frames their potential.... The vehicle has not been in the motion analysis in order to defuse severe traffic.. Found effective and paves the way to the individual criteria the input uses... 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