computer vision based accident detection in traffic surveillance github
Section V illustrates the conclusions of the experiment and discusses future areas of exploration. In the event of a collision, a circle encompasses the vehicles that collided is shown. 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. This results in a 2D vector, representative of the direction of the vehicles motion. 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. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. This section describes our proposed framework given in Figure 2. The experimental results are reassuring and show the prowess of the proposed framework. become a beneficial but daunting task. road-traffic CCTV surveillance footage. 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. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. 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. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. The first version of the You Only Look Once (YOLO) deep learning method was introduced in 2015 [21]. We estimate , the interval between the frames of the video, using the Frames Per Second (FPS) as given in Eq. This architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. 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. 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). 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. The proposed framework is purposely designed with efficient algorithms in order to be applicable in real-time traffic monitoring systems. 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. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. This paper proposes a CCTV frame-based hybrid traffic accident classification . This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. In this paper, a neoteric framework for detection of road accidents is proposed. 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. The dataset includes day-time and night-time videos of various challenging weather and illumination conditions. The existing approaches are optimized for a single CCTV camera through parameter customization. A predefined number (B. ) 2020, 2020. Many people lose their lives in road accidents. 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. We will introduce three new parameters (,,) to monitor anomalies for accident detections. 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. Video processing was done using OpenCV4.0. 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. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. Description Accident Detection in Traffic Surveillance using opencv Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Automatic detection of traffic accidents is an important emerging topic in Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. vehicle-to-pedestrian, and vehicle-to-bicycle. The trajectories of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that can lead to accidents. Figure 4 shows sample accident detection results by our framework given videos containing vehicle-to-vehicle (V2V) side-impact collisions. If you find a rendering bug, file an issue on GitHub. 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. 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. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. Typically, anomaly detection methods learn the normal behavior via training. We can minimize this issue by using CCTV accident detection. 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. detection. The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. Each video clip includes a few seconds before and after a trajectory conflict. An accident Detection System is designed to detect accidents via video or CCTV footage. The framework is built of five modules. 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). The next task in the framework, T2, is to determine the trajectories of the vehicles. arXiv as responsive web pages so you We estimate. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. 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. This paper conducted an extensive literature review on the applications of . 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. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. 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. Fig. We then utilize the output of the neural network to identify road-side vehicular accidents by extracting feature points and creating our own set of parameters which are then used to identify vehicular accidents. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. Surveillance, Detection of road traffic crashes based on collision estimation, Blind-Spot Collision Detection System for Commercial Vehicles Using Want to hear about new tools we're making? The surveillance videos at 30 frames per second (FPS) are considered. 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 . The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Edit social preview. We then determine the magnitude of the vector. The performance is compared to other representative methods in table I. A sample of the dataset is illustrated in Figure 3. 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]. If (L H), is determined from a pre-defined set of conditions on the value of . 7. 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. This is the key principle for detecting an accident. Before running the program, you need to run the accident-classification.ipynb file which will create the model_weights.h5 file. From this point onwards, we will refer to vehicles and objects interchangeably. Section II succinctly debriefs related works and literature. 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 process used to determine, where the bounding boxes of two vehicles overlap goes as follow: Let's first import the required libraries and the modules. Then, to run this python program, you need to execute the main.py python file. 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). Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. different types of trajectory conflicts including vehicle-to-vehicle, Additionally, it keeps track of the location of the involved road-users after the conflict has happened. 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. Nowadays many urban intersections are equipped with accident is determined based on speed and trajectory anomalies in a vehicle In this paper, a new framework to detect vehicular collisions is proposed. The magenta line protruding from a vehicle depicts its trajectory along the direction. Leaving abandoned objects on the road for long periods is dangerous, so . of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. 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. 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. 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. of the proposed framework is evaluated using video sequences collected from The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. 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, Real-Time Accident Detection in Traffic Surveillance Using Deep Learning, Intelligent Intersection: Two-Stream Convolutional Networks for Mask R-CNN is an instance segmentation algorithm that was introduced by He et al. 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. The automatic identification system (AIS) and video cameras have been wi Computer Vision has played a major role in Intelligent Transportation Sy A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, 2016 IEEE international conference on image processing (ICIP), Yolov4: optimal speed and accuracy of object detection, M. O. Faruque, H. Ghahremannezhad, and C. Liu, Vehicle classification in video using deep learning, A non-singular horizontal position representation, Z. Ge, S. Liu, F. Wang, Z. Li, and J. In order to efficiently solve the data association problem despite challenging scenarios, such as occlusion, false positive or false negative results from the object detection, overlapping objects, and shape changes, we design a dissimilarity cost function that employs a number of heuristic cues, including appearance, size, intersection over union (IOU), and position. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. Selecting the region of interest will start violation detection system. Video processing was done using OpenCV4.0. Over a course of the precedent couple of decades, researchers in the fields of image processing and computer vision have been looking at traffic accident detection with great interest [5]. This results in a 2D vector, representative of the direction of the vehicles motion. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. A sample of the dataset is illustrated in Figure 3. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. 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. Scribd is the world's largest social reading and publishing site. 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. Furthermore, Figure 5 contains samples of other types of incidents detected by our framework, including near-accidents, vehicle-to-bicycle (V2B), and vehicle-to-pedestrian (V2P) conflicts. 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