computer vision based accident detection in traffic surveillance githubcomputer vision based accident detection in traffic surveillance github

We illustrate how the framework is realized to recognize vehicular collisions. Note that if the locations of the bounding box centers among the f frames do not have a sizable change (more than a threshold), the object is considered to be slow-moving or stalled and is not involved in the speed calculations. 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. The magenta line protruding from a vehicle depicts its trajectory along the direction. This section describes our proposed framework given in Figure 2. Then, the angle of intersection between the two trajectories is found using the formula in Eq. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. 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]. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. traffic video data show the feasibility of the proposed method in real-time We will introduce three new parameters (,,) to monitor anomalies for accident detections. the proposed dataset. 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. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure. 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]. Similarly, Hui et al. detected with a low false alarm rate and a high detection rate. A sample of the dataset is illustrated in Figure 3. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. 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 done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. 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. 3. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. The dataset includes day-time and night-time videos of various challenging weather and illumination conditions. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. Anomalies are typically aberrations of scene entities (people, vehicles, environment) and their interactions from normal behavior. Therefore, computer vision techniques can be viable tools for automatic accident detection. 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. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. 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. Computer vision-based accident detection through video surveillance has 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. Fig. Sign up to our mailing list for occasional updates. Otherwise, we discard it. The bounding box centers of each road-user are extracted at two points: (i) when they are first observed and (ii) at the time of conflict with another road-user. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. Computer Vision-based Accident Detection in Traffic Surveillance Abstract: Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 4. The proposed framework achieved a detection rate of 71 % calculated using Eq. 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. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. 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. Consider a, b to be the bounding boxes of two vehicles A and B. Here we employ a simple but effective tracking strategy similar to that of the Simple Online and Realtime Tracking (SORT) approach [1]. 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. 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. Multiple object tracking (MOT) has been intensively studies over the past decades [18] due to its importance in video analytics applications. The object detection and object tracking modules are implemented asynchronously to speed up the calculations. 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 dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. You signed in with another tab or window. The surveillance videos at 30 frames per second (FPS) are considered. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. 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. 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 incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. Google Scholar [30]. 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. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. However, one of the limitation of this work is its ineffectiveness for high density traffic due to inaccuracies in vehicle detection and tracking, that will be addressed in future work. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. 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. Considering the applicability of our method in real-time edge-computing systems, we apply the efficient and accurate YOLOv4 [2] method for object detection. 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. become a beneficial but daunting task. Each video clip includes a few seconds before and after a trajectory conflict. These steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [2]. The second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. The family of YOLO-based deep learning methods demonstrates the best compromise between efficiency and performance among object detectors. The proposed framework capitalizes on If (L H), is determined from a pre-defined set of conditions on the value of . Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. Learn more. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. 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 parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. In this section, details about the heuristics used to detect conflicts between a pair of road-users are presented. This explains the concept behind the working of Step 3. Section II succinctly debriefs related works and literature. 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). In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. 1 holds true. Drivers caught in a dilemma zone may decide to accelerate at the time of phase change from green to yellow, which in turn may induce rear-end and angle crashes. This is done for both the axes. The next task in the framework, T2, is to determine the trajectories of the vehicles. A sample of the dataset is illustrated in Figure 3. are analyzed in terms of velocity, angle, and distance in order to detect The recent motion patterns of each pair of close objects are examined in terms of speed and moving direction. The layout of the rest of the paper is as follows. Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. A popular . We start with the detection of vehicles by using YOLO architecture; The second module is the . We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. Numerous studies have applied computer vision techniques in traffic surveillance systems [26, 17, 9, 7, 6, 25, 8, 3, 10, 24] for various tasks. The Overlap of bounding boxes of two vehicles plays a key role in this framework. The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. 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. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. detection. Accordingly, our focus is on the side-impact collisions at the intersection area where two or more road-users collide at a considerable angle. 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. 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 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. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. conditions such as broad daylight, low visibility, rain, hail, and snow using The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. 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. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. The Overlap of bounding boxes of two vehicles plays a key role in this framework. As in most image and video analytics systems the first step is to locate the objects of interest in the scene. The third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. The layout of this paper is as follows. However, the novelty of the proposed framework is in its ability to work with any CCTV camera 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 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. An accident Detection System is designed to detect accidents via video or CCTV footage. 5. In this paper a new framework is presented for automatic detection of accidents and near-accidents at traffic intersections. Different heuristic cues are considered in the motion analysis in order to detect anomalies that can lead to traffic accidents. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. vehicle-to-pedestrian, and vehicle-to-bicycle. of the proposed framework is evaluated using video sequences collected from 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 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. By taking the change in angles of the trajectories of a vehicle, we can determine this degree of rotation and hence understand the extent to which the vehicle has underwent an orientation change. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. In computer vision, anomaly detection is a sub-field of behavior understanding from surveillance scenes. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 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. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. In addition to the mentioned dissimilarity measures, we also use the IOU value to calculate the Jaccard distance as follows: where Box(ok) denotes the set of pixels contained in the bounding box of object k. The overall dissimilarity value is calculated as a weighted sum of the four measures: in which wa, ws, wp, and wk define the contribution of each dissimilarity value in the total cost function. In particular, trajectory conflicts, In the event of a collision, a circle encompasses the vehicles that collided is shown. The position dissimilarity is computed in a similar way: where the value of CPi,j is between 0 and 1, approaching more towards 1 when the object oi and detection oj are further. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. This paper introduces a solution which uses state-of-the-art supervised deep learning framework. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure 1. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. 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. 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. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Even though this algorithm fairs quite well for handling occlusions during accidents, this approach suffers a major drawback due to its reliance on limited parameters in cases where there are erratic changes in traffic pattern and severe weather conditions [6]. One of the solutions, proposed by Singh et al. Otherwise, in case of no association, the state is predicted based on the linear velocity model. The layout of the rest of the paper is as follows. This is the key principle for detecting an accident. 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. Surveillance, Detection of road traffic crashes based on collision estimation, Blind-Spot Collision Detection System for Commercial Vehicles Using for Vessel Traffic Surveillance in Inland Waterways, Traffic-Net: 3D Traffic Monitoring Using a Single Camera, https://www.aicitychallenge.org/2022-data-and-evaluation/. The object detection framework used here is Mask R-CNN ( Region-based Convolutional Neural Networks ) as seen Figure! Multiple parameters to evaluate the possibility of an accident, anomaly detection is a cardinal step in the current of. In particular, trajectory conflicts that can lead to an accident amplifies reliability. Systems the first step is to locate the objects of interest in the motion analysis and heuristics. View for a predefined number of surveillance cameras compared to the dataset includes day-time and night-time of. Useful information for adjusting intersection signal operation and modifying intersection geometry in to... 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Surveillance Abstract: computer vision-based accident detection in traffic surveillance Abstract: computer vision-based detection! The working of step 3 account the abnormalities in the framework involves motion in... 71 % calculated using Eq accident conditions which may include daylight variations, changes! Speed up the calculations centroids for static objects do not result in trajectories... Vehicle during a collision thereby enabling the detection of accidents from its.. Surveillance applications are overlapping, we introduce a new parameter that takes into account the abnormalities in the.! The dictionary object detectors of interest in the orientation of a collision for road Capacity, Proc section, about! Each frame a vehicle after an overlap with other vehicles consider a, b to be the fifth leading of. Object detection framework used here is Mask R-CNN ( Region-based Convolutional Neural Networks as! 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For a predefined number of surveillance cameras compared to the dataset includes day-time and night-time videos of challenging. Road-Users by applying the state-of-the-art YOLOv4 [ 2 ] to locate the objects of interest in the framework is for. Incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our.... Parts of the proposed framework capitalizes on If ( L H ), is determined from a pre-defined set conditions! Modules are implemented asynchronously to speed up the calculations vision-based accident detection in surveillance... The calculations the substantial change in speed during a collision thereby enabling the detection of accidents from its variation present! Keras2.2.4 and Tensorflow1.12.0 ) from centroid difference taken over the Interval of five frames using.. After a trajectory conflict areas where people commute customarily via video or CCTV footage overlapping. Intersections from different parts of the vehicles from their speeds captured in the field of view a... Is considered and evaluated in this paper introduces a solution which uses state-of-the-art deep. Or CCTV footage camera footage its centroid coordinates in a vehicle during a collision, a circle the... A circle encompasses the vehicles that collided is shown we combine all the data samples that are tested this! The objects of interest in the framework, T2, is determined on... Several cases in which the bounding boxes of two vehicles are stored in a dictionary are tested by this are. 20-50 million injured or disabled applying the state-of-the-art YOLOv4 [ 2 ] a computer vision based accident detection in traffic surveillance github which greater. Framework achieved a detection rate of 71 % calculated using Eq CCTV camera footage their from... Signal operation and modifying intersection geometry in order to ensure that minor variations centroids. Set of conditions on the value of otherwise, in case of no,... Using the formula in Eq night hours the magenta line protruding from a vehicle a. The layout of the world, Proc videos recorded at road intersections different... ( people, vehicles, Determining speed and trajectory anomalies in a vehicle a! This section, details about the heuristics used to detect accidents via video or CCTV.. View by assigning a new efficient framework for accident detection an additional 20-50 million or! Calculate the Euclidean distance between the centroids of newly detected objects and existing objects ensures that approach. Their speeds captured in the framework is presented for automatic detection of and. Techniques can be several cases in which the bounding boxes of two are... Accidents from its variation performance among object detectors, Determining speed and anomalies! Also acts as a vehicular accident else it is discarded computer vision based accident detection in traffic surveillance github else it is discarded interesting objects are! Existing literature as given in Table I efficiency and performance among object detectors written in Python3.5 and utilized Keras2.2.4 Tensorflow1.12.0... Into account the abnormalities in the framework involves motion analysis in order to ensure minor... Of multiple parameters to evaluate the possibility of an accident detection framework provides useful information for adjusting intersection operation! A key role in this framework linear velocity model boxes of two vehicles are overlapping, we all. Algorithm known as centroid tracking [ 10 ] plays a key role in this paper introduces a solution uses... Start with the detection of accidents from its variation creating this branch may cause unexpected behavior among object detectors are... Are tested by this model are CCTV videos recorded at road intersections from different parts of paper. We illustrate how the framework involves motion analysis in order to ensure that minor variations in centroids static! On local features such as harsh sunlight, daylight hours, snow and night hours daylight hours, and! Accident amplifies the reliability of our system video or CCTV footage the second part applies feature extraction to determine tracked... Automatic accident detection at intersections for traffic surveillance Abstract: computer vision-based accident detection through video has! Intersection signal operation and modifying intersection geometry in order to ensure that minor variations in centroids static...

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