The existing approaches are optimized for a single CCTV camera through parameter customization. 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 proposed framework achieved a detection rate of 71 % calculated using Eq. 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. Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. Therefore, computer vision techniques can be viable tools for automatic accident detection. Here we employ a simple but effective tracking strategy similar to that of the Simple Online and Realtime Tracking (SORT) approach [1]. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. We determine the speed of the vehicle in a series of steps. In the event of a collision, a circle encompasses the vehicles that collided is shown. Otherwise, we discard it. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. We determine the speed of the vehicle in a series of steps. This architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. What is Accident Detection System? This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. Section IV contains the analysis of our experimental results. 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 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 Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. 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. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. We then determine the magnitude of the vector, , as shown in Eq. The surveillance videos at 30 frames per second (FPS) are considered. dont have to squint at a PDF. Sign up to our mailing list for occasional updates. detected with a low false alarm rate and a high detection rate. method to achieve a high Detection Rate and a low False Alarm Rate on general While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. 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). Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. Each video clip includes a few seconds before and after a trajectory conflict. After that administrator will need to select two points to draw a line that specifies traffic signal. The object trajectories The spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. 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. traffic video data show the feasibility of the proposed method in real-time Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. traffic monitoring systems. 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. Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. [4]. 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. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure 1. 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. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. 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. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. Currently, I am experimenting with cutting-edge technology to unleash cleaner energy sources to power the world.<br>I have a total of 8 . This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. 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. Multiple object tracking (MOT) has been intensively studies over the past decades [18] due to its importance in video analytics applications. You can also use a downloaded video if not using a camera. 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. 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. Mask R-CNN improves upon Faster R-CNN [12] by using a new methodology named as RoI Align instead of using the existing RoI Pooling which provides 10% to 50% more accurate results for masks[4]. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. This explains the concept behind the working of Step 3. This section describes our proposed framework given in Figure 2. Then, to run this python program, you need to execute the main.py python file. 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. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. conditions such as broad daylight, low visibility, rain, hail, and snow using Currently, most traffic management systems monitor the traffic surveillance camera by using manual perception of the captured footage. different types of trajectory conflicts including vehicle-to-vehicle, In this paper, a neoteric framework for detection of road accidents is proposed. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. As illustrated in fig. 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 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. Are you sure you want to create this branch? 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 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. This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. 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. However, it suffers a major drawback in accurate predictions when determining accidents in low-visibility conditions, significant occlusions in car accidents, and large variations in traffic patterns [15]. Note: This project requires a camera. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. 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. Therefore, for this study we focus on the motion patterns of these three major road-users to detect the time and location of trajectory conflicts. The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. 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 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. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. Current traffic management technologies heavily rely on human perception of the footage that was captured. The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. applied for object association to accommodate for occlusion, overlapping 8 and a false alarm rate of 0.53 % calculated using Eq. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: 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. In this paper, a neoteric framework for detection of road accidents is proposed. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. 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 trajectories are further analyzed to monitor the motion patterns of the detected road-users in terms of location, speed, and moving direction. This is done for both the axes. 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. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. The experimental results are reassuring and show the prowess of the proposed framework. 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). Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. Section II succinctly debriefs related works and literature. This paper proposes a CCTV frame-based hybrid traffic accident classification . , " A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition," Journal of advanced transportation, vol. 9. A sample of the dataset is illustrated in Figure 3. Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. Description Accident Detection in Traffic Surveillance using opencv Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 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. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. Dhananjai Chand2, Savyasachi Gupta 3, Goutham K 4, Assistant Professor, Department of Computer Science and Engineering, B.Tech., Department of Computer Science and Engineering, 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. 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. 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. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. the development of general-purpose vehicular accident detection algorithms in Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. 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. 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 framework integrates three major modules, including object detection based on YOLOv4 method, a tracking method based on Kalman filter and Hungarian algorithm with a new cost function, and an accident detection module to analyze the extracted trajectories for anomaly detection. The Overlap of bounding boxes of two vehicles plays a key role in this 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. 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. This framework was found effective and paves the way to detect anomalies such as traffic accidents in real time. 1: The system architecture of our proposed accident detection framework. The proposed framework achieved a detection rate of 71 % calculated using Eq. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. We then display this vector as trajectory for a given vehicle by extrapolating it. computer vision techniques can be viable tools for automatic accident We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. Therefore, computer vision techniques can be viable tools for automatic accident detection. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 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