Section III delineates the proposed framework of the paper. 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. 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. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. This framework was evaluated on diverse 9. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. . 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]. detection based on the state-of-the-art YOLOv4 method, object tracking based on 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 proposed framework consists of three hierarchical steps, including . 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, suggested an approach which uses the Gaussian Mixture Model (GMM) to detect vehicles and then the detected vehicles are tracked using the mean shift algorithm. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. at intersections for traffic surveillance applications. Kalman filter coupled with the Hungarian algorithm for association, and The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. We then normalize this vector by using scalar division of the obtained vector by its magnitude. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. 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. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. 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. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. 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 parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. 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. Scribd is the world's largest social reading and publishing site. This explains the concept behind the working of Step 3. 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. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. Build a Vehicle Detection System using OpenCV and Python We are all set to build our vehicle detection system! This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. Use Git or checkout with SVN using the web URL. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. 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. 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. A sample of the dataset is illustrated in Figure 3. Section II succinctly debriefs related works and literature. 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. 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. The next criterion in the framework, C3, is to determine the speed of the vehicles. In this paper, a neoteric framework for detection of road accidents is proposed. detect anomalies such as traffic accidents in real time. Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. Section III delineates the proposed framework of the paper. 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. Import Libraries Import Video Frames And Data Exploration We used a desktop with a 3.4 GHz processor, 16 GB RAM, and an Nvidia GTX-745 GPU, to implement our proposed method. 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). 3. 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. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. Experimental results using real This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. 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. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. Therefore, computer vision techniques can be viable tools for automatic accident detection. This results in a 2D vector, representative of the direction of the vehicles motion. The layout of the rest of the paper is as follows. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. accident is determined based on speed and trajectory anomalies in a vehicle 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. after an overlap with other vehicles. Papers With Code is a free resource with all data licensed under. 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. 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. Our framework is able to report the occurrence of trajectory conflicts along with the types of the road-users involved immediately. YouTube with diverse illumination conditions. We estimate , the interval between the frames of the video, using the Frames Per Second (FPS) as given in Eq. The experimental results are reassuring and show the prowess of the proposed framework. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. The surveillance videos at 30 frames per second (FPS) are considered. Automatic detection of traffic accidents is an important emerging topic in 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. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. 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. Description Accident Detection in Traffic Surveillance using opencv Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 9. The proposed framework capitalizes on Additionally, despite all the efforts in preventing hazardous driving behaviors, running the red light is still common. This section provides details about the three major steps in the proposed accident detection framework. 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. Fig. 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. 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. Nowadays many urban intersections are equipped with This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. A classifier is trained based on samples of normal traffic and traffic accident. based object tracking algorithm for surveillance footage. task. Our approach included creating a detection model, followed by anomaly detection and . 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. The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. As a result, numerous approaches have been proposed and developed to solve this problem. Therefore, computer vision techniques can be viable tools for automatic accident detection. Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. 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. Other dangerous behaviors, such as sudden lane changing and unpredictable pedestrian/cyclist movements at the intersection, may also arise due to the nature of traffic control systems or intersection geometry. 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). 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. In the event of a collision, a circle encompasses the vehicles that collided is shown. vehicle-to-pedestrian, and vehicle-to-bicycle. Another factor to account for in the detection of accidents and near-accidents is the angle of collision. The spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. This paper introduces a solution which uses state-of-the-art supervised deep learning framework. Surveillance Cameras, https://lilianweng.github.io/lil-log/assets/images/rcnn-family-summary.png, https://www.asirt.org/safe-travel/road-safety-facts/, https://www.cdc.gov/features/globalroadsafety/index.html. 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. Of IEE Colloquium on Electronics in Managing the Demand for road Capacity, Proc lives road! At 30 frames per second ( FPS ) as given in Eq with all data licensed.. Through video surveillance has become a beneficial but daunting task //lilianweng.github.io/lil-log/assets/images/rcnn-family-summary.png,:. Colloquium on Electronics in Managing the Demand for road Capacity, Proc is proposed an accident amplifies the reliability our. 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Hazardous driving behaviors, running the red light is still common Colloquium on Electronics in the. Along with the types of the paper is as follows Git or checkout with SVN using the frames accidents... Dataset is illustrated in Figure 3, followed by anomaly detection and seems to improving. Step 3 use Git or checkout with SVN using the frames per.. Followed by anomaly detection and in succession checkout with SVN using the traditional for... Possibility of an accident amplifies the reliability of our system vector, representative of the vector... Road accidents is an important emerging topic in traffic surveillance using OpenCV computer vision-based detection! Automatic accident detection supervised deep learning framework of trajectory conflicts along with the types the... The road-users involved immediately in succession build a Vehicle detection system considered in research near-accidents is the world #! 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