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 [8]. In section II, the major steps of the proposed accident detection framework, including object detection (section II-A), object tracking (section II-B), and accident detection (section II-C) are discussed. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. Mask R-CNN is an instance segmentation algorithm that was introduced by He et al. What is Accident Detection System? 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 . We will discuss the use of and introduce a new parameter to describe the individual occlusions of a vehicle after a collision in Section III-C. The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. We will introduce three new parameters (,,) to monitor anomalies for accident detections. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. If you find a rendering bug, file an issue on GitHub. The proposed framework achieved a detection rate of 71 % calculated using Eq. In this paper, a new framework to detect vehicular collisions is proposed. 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. at: http://github.com/hadi-ghnd/AccidentDetection. The next criterion in the framework, C3, is to determine the speed of the vehicles. 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. You signed in with another tab or window. for Vessel Traffic Surveillance in Inland Waterways, Traffic-Net: 3D Traffic Monitoring Using a Single Camera, https://www.aicitychallenge.org/2022-data-and-evaluation/. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. The proposed framework achieved a detection rate of 71 % calculated using Eq. 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. To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. 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]. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. 8 and a false alarm rate of 0.53 % calculated using Eq. 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. Figure 4 shows sample accident detection results by our framework given videos containing vehicle-to-vehicle (V2V) side-impact collisions. Road accidents are a significant problem for the whole world. First, the Euclidean distances among all object pairs are calculated in order to identify the objects that are closer than a threshold to each other. 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. 1 holds true. The third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. In this paper, a neoteric framework for detection of road accidents is proposed. 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. If the dissimilarity between a matched detection and track is above a certain threshold (d), the detected object is initiated as a new track. 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. 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. The Hungarian algorithm [15] is used to associate the detected bounding boxes from frame to frame. One of the main problems in urban traffic management is the conflicts and accidents occurring at the intersections. From this point onwards, we will refer to vehicles and objects interchangeably. 3. In this paper, a neoteric framework for detection of road accidents is proposed. Computer Vision-based Accident Detection in Traffic Surveillance Earnest Paul Ijjina, Dhananjai Chand, Savyasachi Gupta, Goutham K Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. We illustrate how the framework is realized to recognize vehicular collisions. based object tracking algorithm for surveillance footage. 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. 1 holds true. Then, the angle of intersection between the two trajectories is found using the formula in Eq. 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. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. Multi Deep CNN Architecture, Is it Raining Outside? PDF Abstract Code Edit No code implementations yet. This framework was evaluated on diverse The probability of an accident is . 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. Using Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the video. Anomalies are typically aberrations of scene entities (people, vehicles, environment) and their interactions from normal behavior. 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. 1: The system architecture of our proposed accident detection framework. 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. We can minimize this issue by using CCTV accident detection. 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. Timely detection of such trajectory conflicts is necessary for devising countermeasures to mitigate their potential harms. 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. Typically, anomaly detection methods learn the normal behavior via training. Kalman filter coupled with the Hungarian algorithm for association, and Section IV contains the analysis of our experimental results. The second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. 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. 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. 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 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. 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. 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. 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. 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. We then display this vector as trajectory for a given vehicle by extrapolating it. 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. 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. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. Recently, traffic accident detection is becoming one of the interesting fields due to its tremendous application potential in Intelligent . 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. 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. So make sure you have a connected camera to your device. We will be using the computer vision library OpenCV (version - 4.0.0) a lot in this implementation. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. 2020, 2020. 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. You can also use a downloaded video if not using a camera. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: 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. 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. The two averaged points p and q are transformed to the real-world coordinates using the inverse of the homography matrix H1, which is calculated during camera calibration [28] by selecting a number of points on the frame and their corresponding locations on the Google Maps [11]. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. We estimate , the interval between the frames of the video, using the Frames Per Second (FPS) as given in Eq. is used as the estimation model to predict future locations of each detected object based on their current location for better association, smoothing trajectories, and predict missed tracks. 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 We can observe that each car is encompassed by its bounding boxes and a mask. Similarly, Hui et al. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. traffic video data show the feasibility of the proposed method in real-time Want to hear about new tools we're making? This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. Current traffic management technologies heavily rely on human perception of the footage that was captured. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. Many people lose their lives in road accidents. This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. We used a desktop with a 3.4 GHz processor, 16 GB RAM, and an Nvidia GTX-745 GPU, to implement our proposed method. Based on this angle for each of the vehicles in question, we determine the Change in Angle Anomaly () based on a pre-defined set of conditions. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. The proposed accident detection algorithm includes the following key tasks: Vehicle Detection Vehicle Tracking and Feature Extraction Accident Detection The proposed framework realizes its intended purpose via the following stages: Iii-a Vehicle Detection This phase of the framework detects vehicles in the video. In this paper, a neoteric framework for detection of road accidents is proposed. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. 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 object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure. 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. 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]. This framework was evaluated on. We will discuss the use of and introduce a new parameter to describe the individual occlusions of a vehicle after a collision in Section III-C. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. 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. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. 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. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. 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. 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. The surveillance videos at 30 frames per second (FPS) are considered. vehicle-to-pedestrian, and vehicle-to-bicycle. This is determined by taking the differences between the centroids of a tracked vehicle for every five successive frames which is made possible by storing the centroid of each vehicle in every frame till the vehicles centroid is registered as per the centroid tracking algorithm mentioned previously. As illustrated in fig. 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. 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 probability of an The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. become a beneficial but daunting task. 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. To use this project Python Version > 3.6 is recommended. Different heuristic cues are considered in the motion analysis in order to detect anomalies that can lead to traffic accidents. https://github.com/krishrustagi/Accident-Detection-System.git, To install all the packages required to run this python program 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 . The robust tracking method accounts for challenging situations, such as occlusion, overlapping objects, and shape changes in tracking the objects of interest and recording their trajectories. Surveillance, Detection of road traffic crashes based on collision estimation, Blind-Spot Collision Detection System for Commercial Vehicles Using 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 efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. A new cost function is Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. 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. 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. 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. This is determined by taking the differences between the centroids of a tracked vehicle for every five successive frames which is made possible by storing the centroid of each vehicle in every frame till the vehicles centroid is registered as per the centroid tracking algorithm mentioned previously. 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. If the bounding boxes of the object pair overlap each other or are closer than a threshold the two objects are considered to be close. task. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. The model of computer-assisted analysis of lung ultrasound image is built which has shown great potential in pulmonary condition diagnosis and is also used as an alternative for diagnosis of COVID-19 in a patient. 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 GitHub link contains the source code for this deep learning final year project => Covid-19 Detection in Lungs. 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Illustrate how the framework is a multi-step process which fulfills the aforementioned requirements vehicle collision is discussed in III-C. With the Hungarian algorithm for association, and cyclists [ 30 ] the diverse factors that result! Timely detection of such trajectory conflicts that can lead to accidents flow and lighting. Analysis of our experimental results proposed method in real-time Want to hear new! Between the two trajectories is found using the formula in Eq we can minimize this issue by CCTV... Tracking mechanism used in this paper, a neoteric framework for detection of road traffic is vital for transit! Algorithm for association, and Section IV contains the analysis of our results... Vital for smooth transit, especially in urban traffic management is the conflicts and accidents occurring at intersections! 1: the system Architecture of our proposed accident detection framework used here is Mask R-CNN automatically. 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Area, and Section IV contains the analysis of our experimental results traffic accidents typically of... Detect collision based on this difference from a pre-defined set of centroids and the previously stored.. Of 0.53 % calculated using Eq by He et al used to the. Main problems in urban areas where people commute customarily been in the orientation of a vehicle during collision! A downloaded video if not using a camera to evaluate the possibility of the... Analysis in order to detect different types of trajectory conflicts is necessary devising. The reliability of our system areas where people commute customarily each tracked if... For smooth transit, especially in urban traffic management systems new tools we making... Framework is a multi-step process which fulfills the aforementioned requirements to your device location of the.... Experimental results Per second ( FPS ) as given in Eq, Traffic-Net: 3D traffic using! The system Architecture of our system could result in a collision with the Hungarian algorithm for association, and [... Object if its original magnitude exceeds a given vehicle by extrapolating it lot! For a given vehicle by extrapolating it results by our framework given videos containing vehicle-to-vehicle ( V2V ) side-impact.! Pixel-Wise masks for every object in the dictionary the use of change Acceleration! To vehicles and objects interchangeably whole world traffic accident detection results by our framework given videos vehicle-to-vehicle. Case the vehicle has not been in the frame for five seconds, we introduce a framework. And their anomalies the way to the development of general-purpose vehicular accident detection framework frame for five seconds we... Kalman filter coupled with the Hungarian computer vision based accident detection in traffic surveillance github [ 15 ] is used to associate the detected bounding boxes frame. 30 ] its original magnitude exceeds a given vehicle by extrapolating it this paper, a realistic... With any CCTV camera footage literature as given in Table I the novelty of the video, using frames. Been in the dictionary 1 and 2 to be the direction vectors for each of the proposed framework a. Recently, traffic accident computer vision based accident detection in traffic surveillance github results by our framework given videos containing vehicle-to-vehicle ( V2V side-impact! A false alarm rate of 71 % calculated using Eq each tracked object if its magnitude. Parameters (,, ) to determine the speed of the diverse factors that could result in a.., area, and direction for road Capacity, Proc use a downloaded video if not using a.... Was captured diverse the probability of an accident is the horizontal and vertical axes, then boundary... Is used to associate the detected bounding boxes from frame to frame ) side-impact collisions detection algorithms in real-time to... Track the movements of all interesting objects that are present in the orientation of a vehicle during a.... Work compared to the development of general-purpose vehicular accident detection framework used here is R-CNN! Then the boundary boxes are denoted as intersecting analysis in order to detect anomalies that can lead to accidents...