The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. real-time. 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. We can minimize this issue by using CCTV accident detection. In this paper, a new framework to detect vehicular collisions is proposed. 9. The proposed framework provides a robust We can minimize this issue by using CCTV accident detection. The average processing speed is 35 frames per second (fps) which is feasible for real-time applications. This section describes our proposed framework given in Figure 2. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. 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 paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. In recent times, vehicular accident detection has become a prevalent field for utilizing computer vision [5] to overcome this arduous task of providing first-aid services on time without the need of a human operator for monitoring such event. 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. If you find a rendering bug, file an issue on GitHub. 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. 3. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. 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. 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 proposed framework achieved a detection rate of 71 % calculated using Eq. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. based object tracking algorithm for surveillance footage. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. For everything else, email us at [emailprotected]. This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. 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. This paper proposes a CCTV frame-based hybrid traffic accident classification . 5. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. 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. 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. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. 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. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. Due to the lack of a publicly available benchmark for traffic accidents at urban intersections, we collected 29 short videos from YouTube that contain 24 vehicle-to-vehicle (V2V), 2 vehicle-to-bicycle (V2B), and 3 vehicle-to-pedestrian (V2P) trajectory conflict cases. Edit social preview. 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. Vision-based frameworks for Object Detection, Multiple Object Tracking, and Traffic Near Accident Detection are important applications of Intelligent Transportation System, particularly in video surveillance and etc. Accident Detection, Mask R-CNN, Vehicular Collision, Centroid based Object Tracking, Earnest Paul Ijjina1 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]. This paper introduces a solution which uses state-of-the-art supervised deep learning framework. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. 2. 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. A predefined number (B. ) at: http://github.com/hadi-ghnd/AccidentDetection. 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 . Abandoned objects detection is one of the most crucial tasks in intelligent visual surveillance systems, especially in highway scenes [6, 15, 16].Various types of abandoned objects may be found on the road, such as vehicle parts left behind in a car accident, cargo dropped from a lorry, debris dropping from a slope, etc. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. 2020, 2020. . There was a problem preparing your codespace, please try again. 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. Section III provides details about the collected dataset and experimental results and the paper is concluded in section section IV. This paper presents a new efficient framework for accident detection at intersections . As a result, numerous approaches have been proposed and developed to solve this problem. Authors: Authors: Babak Rahimi Ardabili, Armin Danesh Pazho, Ghazal Alinezhad Noghre, Christopher Neff, Sai Datta Bhaskararayuni, Arun Ravindran, Shannon Reid, Hamed Tabkhi Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computer Vision and . 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. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. 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 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. Each video clip includes a few seconds before and after a trajectory conflict. The layout of this paper is as follows. We used a desktop with a 3.4 GHz processor, 16 GB RAM, and an Nvidia GTX-745 GPU, to implement our proposed method. 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. Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. In this paper, a neoteric framework for detection of road accidents is proposed. surveillance cameras connected to traffic management systems. If (L H), is determined from a pre-defined set of conditions on the value of . If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. So make sure you have a connected camera to your device. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. 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. Are you sure you want to create this branch? This section describes our proposed framework given in Figure 2. for smoothing the trajectories and predicting missed objects. 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 Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. What is Accident Detection System? accident is determined based on speed and trajectory anomalies in a vehicle Therefore, computer vision techniques can be viable tools for automatic accident detection. The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. Accordingly, our focus is on the side-impact collisions at the intersection area where two or more road-users collide at a considerable angle. 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 probability of an Another factor to account for in the detection of accidents and near-accidents is the angle of collision. The proposed framework is purposely designed with efficient algorithms in order to be applicable in real-time traffic monitoring systems. Therefore, Then, the angle of intersection between the two trajectories is found using the formula in Eq. Recently, traffic accident detection is becoming one of the interesting fields due to its tremendous application potential in Intelligent . 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. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. Or, have a go at fixing it yourself the renderer is open source! Mask R-CNN for accurate object detection followed by an efficient centroid This explains the concept behind the working of Step 3. 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. 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. Multi Deep CNN Architecture, Is it Raining Outside? Additionally, it performs unsatisfactorily because it relies only on trajectory intersections and anomalies in the traffic flow pattern, which indicates that it wont perform well in erratic traffic patterns and non-linear trajectories. 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. This is done for both the axes. 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