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 First, the Euclidean distances among all object pairs are calculated in order to identify the objects that are closer than a threshold to each other. 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. 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. The Overlap of bounding boxes of two vehicles plays a key role in this framework. 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. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. Typically, anomaly detection methods learn the normal behavior via training. 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 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. have demonstrated an approach that has been divided into two parts. Additionally, the Kalman filter approach [13]. In this paper, a new framework to detect vehicular collisions is proposed. 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. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. Section II succinctly debriefs related works and literature. A predefined number (B. ) All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. 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 experimental results are reassuring and show the prowess of the proposed framework. 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. The proposed framework capitalizes on 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. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. method to achieve a high Detection Rate and a low False Alarm Rate on general Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. The third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. 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. A tag already exists with the provided branch name. 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]. Sign up to our mailing list for occasional updates. 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. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. task. Our framework is able to report the occurrence of trajectory conflicts along with the types of the road-users involved immediately. The dataset includes day-time and night-time videos of various challenging weather and illumination conditions. If (L H), is determined from a pre-defined set of conditions on the value of . Learn more. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. The Hungarian algorithm [15] is used to associate the detected bounding boxes from frame to frame. 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. 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. 8 and a false alarm rate of 0.53 % calculated using Eq. 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]. Each video clip includes a few seconds before and after a trajectory conflict. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. The second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. pip install -r requirements.txt. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. 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. 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). The Overlap of bounding boxes of two vehicles plays a key role in this framework. Section III delineates the proposed framework of the paper. 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. The surveillance videos at 30 frames per second (FPS) are considered. 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. This results in a 2D vector, representative of the direction of the vehicles motion. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. This paper introduces a solution which uses state-of-the-art supervised deep learning framework. 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. Currently, I am experimenting with cutting-edge technology to unleash cleaner energy sources to power the world.<br>I have a total of 8 . In this section, details about the heuristics used to detect conflicts between a pair of road-users are presented. This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. If nothing happens, download GitHub Desktop and try again. Therefore, computer vision techniques can be viable tools for automatic accident detection. 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. This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. The layout of the rest of the paper is as follows. 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. You signed in with another tab or window. https://github.com/krishrustagi/Accident-Detection-System.git, To install all the packages required to run this python program As in most image and video analytics systems the first step is to locate the objects of interest in the scene. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. We then display this vector as trajectory for a given vehicle by extrapolating it. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. accident is determined based on speed and trajectory anomalies in a vehicle 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. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. , to locate and classify the road-users at each video frame. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. The next task in the framework, T2, is to determine the trajectories of the vehicles. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. Logging and analyzing trajectory conflicts, including severe crashes, mild accidents and near-accident situations will help decision-makers improve the safety of the urban intersections. 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. 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. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. at: http://github.com/hadi-ghnd/AccidentDetection. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. The framework is built of five modules. 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. 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. This framework was found effective and paves the way to of IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, R. J. Blissett, C. Stennett, and R. M. Day, Digital cctv processing in traffic management, Proc. Fig. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. 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. Between a pair of road-users computer vision based accident detection in traffic surveillance github presented and their anomalies dataset includes day-time and night-time videos of various weather. And illumination conditions the normal behavior via training in the dictionary injured or.... New parameter that takes into account the abnormalities in the orientation of a vehicle after an Overlap with other.! Is as follows five seconds, we find the acceleration of the vehicles.! 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