computer vision based accident detection in traffic surveillance github
This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. 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. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. You can also use a downloaded video if not using a camera. The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. Then the approaching angle of the a pair of road-users a and b is calculated as follows: where denotes the estimated approaching angle, ma and mb are the the general moving slopes of the road-users a and b with respect to the origin of the video frame, xta, yta, xtb, ytb represent the center coordinates of the road-users a and b at the current frame, xta and yta are the center coordinates of object a when first observed, xtb and ytb are the center coordinates of object b when first observed, respectively. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. Current traffic management technologies heavily rely on human perception of the footage that was captured. 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. This is the key principle for detecting an accident. The object trajectories The proposed framework provides a robust The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. 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. Figure 4 shows sample accident detection results by our framework given videos containing vehicle-to-vehicle (V2V) side-impact collisions. 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. Learn more. Section III delineates the proposed framework of the paper. The velocity components are updated when a detection is associated to a target. based object tracking algorithm for surveillance footage. https://github.com/krishrustagi/Accident-Detection-System.git, To install all the packages required to run this python program of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. arXiv as responsive web pages so you 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 process used to determine, where the bounding boxes of two vehicles overlap goes as follow: 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. at: http://github.com/hadi-ghnd/AccidentDetection. This framework was found effective and paves the way to Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. The surveillance videos at 30 frames per second (FPS) are considered. Therefore, computer vision techniques can be viable tools for automatic accident detection. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. Let's first import the required libraries and the modules. 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]. 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. Therefore, The first version of the You Only Look Once (YOLO) deep learning method was introduced in 2015 [21]. 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. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. sign in The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. In later versions of YOLO [22, 23] multiple modifications have been made in order to improve the detection performance while decreasing the computational complexity of the method. We will be using the computer vision library OpenCV (version - 4.0.0) a lot in this implementation. Before running the program, you need to run the accident-classification.ipynb file which will create the model_weights.h5 file. In this paper a new framework is presented for automatic detection of accidents and near-accidents at traffic intersections. 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]. If (L H), is determined from a pre-defined set of conditions on the value of . 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. In this paper, a neoteric framework for detection of road accidents is proposed. An accident Detection System is designed to detect accidents via video or CCTV footage. Considering the applicability of our method in real-time edge-computing systems, we apply the efficient and accurate YOLOv4 [2] method for object detection. Another factor to account for in the detection of accidents and near-accidents is the angle of collision. The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. 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). are analyzed in terms of velocity, angle, and distance in order to detect In this paper, a new framework to detect vehicular collisions is proposed. Section III provides details about the collected dataset and experimental results and the paper is concluded in section section IV. 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 this paper, we propose a Decision-Tree enabled approach powered by deep learning for extracting anomalies from traffic cameras while accurately estimating the start and end times of the anomalous event. 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. The magenta line protruding from a vehicle depicts its trajectory along the direction. 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. The family of YOLO-based deep learning methods demonstrates the best compromise between efficiency and performance among object detectors. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. 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. These object pairs can potentially engage in a conflict and they are therefore, chosen for further analysis. 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. Automatic detection of traffic accidents is an important emerging topic in 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 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. This section provides details about the three major steps in the proposed accident detection framework. 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 inter-frame displacement of each detected object is estimated by a linear velocity model. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. 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. In this paper, a neoteric framework for detection of road accidents is proposed. This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. 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. 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. 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. The proposed framework capitalizes on Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. Many people lose their lives in road accidents. 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. 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 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. 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. We will introduce three new parameters (,,) to monitor anomalies for accident detections. Fig. Detection of Rainfall using General-Purpose This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. 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. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. 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 use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. 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. Our preeminent goal is to provide a simple yet swift technique for solving the issue of traffic accident detection which can operate efficiently and provide vital information to concerned authorities without time delay. 5. This paper conducted an extensive literature review on the applications of . A popular . This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. the proposed dataset. We will introduce three new parameters (,,) to monitor anomalies for accident detections. 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. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. The experimental results are reassuring and show the prowess of the proposed framework. This framework was evaluated on diverse 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]. This explains the concept behind the working of Step 3. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. These steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [2]. Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. 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. 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. Import Libraries Import Video Frames And Data Exploration YouTube with diverse illumination conditions. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. 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 this . An accident Detection System is designed to detect accidents via video or CCTV footage. One of the main problems in urban traffic management is the conflicts and accidents occurring at the intersections. As a result, numerous approaches have been proposed and developed to solve this problem. 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. 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. Typically, anomaly detection methods learn the normal behavior via training. 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