Detection of Rainfall using General-Purpose 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. 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. Multi Deep CNN Architecture, Is it Raining Outside? In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. 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 techniques can be viable tools for automatic accident A predefined number (B. ) If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. Traffic closed-circuit television (CCTV) devices can be used to detect and track objects on roads by designing and applying artificial intelligence and deep learning models. A classifier is trained based on samples of normal traffic and traffic accident. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. 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 This paper presents a new efficient framework for accident detection We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. The proposed framework is purposely designed with efficient algorithms in order to be applicable in real-time traffic monitoring systems. One of the main problems in urban traffic management is the conflicts and accidents occurring at the intersections. 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 probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. An accident Detection System is designed to detect accidents via video or CCTV footage. Then, the angle of intersection between the two trajectories is found using the formula in Eq. Want to hear about new tools we're making? In this paper a new framework is presented for automatic detection of accidents and near-accidents at traffic intersections. Note: This project requires a camera. Road accidents are a significant problem for the whole world. 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. of bounding boxes and their corresponding confidence scores are generated for each cell. 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. Section IV contains the analysis of our experimental results. For everything else, email us at [emailprotected]. Sign up to our mailing list for occasional updates. 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. Abstract: In Intelligent Transportation System, real-time systems that monitor and analyze road users become increasingly critical as we march toward the smart city era. 2. , the architecture of this version of YOLO is constructed with a CSPDarknet53 model as backbone network for feature extraction followed by a neck and a head part. the development of general-purpose vehicular accident detection algorithms in at: http://github.com/hadi-ghnd/AccidentDetection. The robustness 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 surveillance videos at 30 frames per second (FPS) are considered. have demonstrated an approach that has been divided into two parts. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. The surveillance videos at 30 frames per second (FPS) are considered. Computer vision-based accident detection through video surveillance has 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. 3. Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. We then normalize this vector by using scalar division of the obtained vector by its magnitude. In this . Add a Anomalies are typically aberrations of scene entities (people, vehicles, environment) and their interactions from normal behavior. This framework was evaluated on. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. The layout of this paper is as follows. In the event of a collision, a circle encompasses the vehicles that collided is shown. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. 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 Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. PDF Abstract Code Edit No code implementations yet. 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 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. 8 and a false alarm rate of 0.53 % calculated using Eq. 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. Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. The total cost function is used by the Hungarian algorithm [15] to assign the detected objects at the current frame to the existing tracks. 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. 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. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. detection of road accidents is proposed. 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. Many people lose their lives in road accidents. 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. Selecting the region of interest will start violation detection system. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. conditions such as broad daylight, low visibility, rain, hail, and snow using Open navigation menu. 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]. If nothing happens, download Xcode and try again. In the UAV-based surveillance technology, video segments captured from . This section describes our proposed framework given in Figure 2. Our approach included creating a detection model, followed by anomaly detection and . We can observe that each car is encompassed by its bounding boxes and a mask. 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). A sample of the dataset is illustrated in Figure 3. 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. In the area of computer vision, deep neural networks (DNNs) have been used to analyse visual events by learning the spatio-temporal features from training samples. 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 proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. In this paper, a neoteric framework for detection of road accidents is proposed. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. Consider a, b to be the bounding boxes of two vehicles A and B. 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. 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). A popular . A new set of dissimilarity measures are designed and used by the Hungarian algorithm [15] for object association coupled with the Kalman filter approach [13]. If you find a rendering bug, file an issue on GitHub. 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, 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. In this paper, we propose a Decision-Tree enabled approach powered by deep learning for extracting anomalies from traffic cameras while accurately estimating the start and end times of the anomalous event. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. Otherwise, we discard it. Mask R-CNN not only provides the advantages of Instance Segmentation but also improves the core accuracy by using RoI Align algorithm. This is done for both the axes. after an overlap with other vehicles. 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. Nowadays many urban intersections are equipped with The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. 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. Additionally, it keeps track of the location of the involved road-users after the conflict has happened. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. 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. Otherwise, in case of no association, the state is predicted based on the linear velocity model. 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. The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. These steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [2]. 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. In this paper, a new framework to detect vehicular collisions is proposed. objects, and shape changes in the object tracking step. 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 architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. In this paper, a neoteric framework for detection of road accidents is proposed. 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. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. Before running the program, you need to run the accident-classification.ipynb file which will create the model_weights.h5 file. 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. The probability of an Figure 4 shows sample accident detection results by our framework given videos containing vehicle-to-vehicle (V2V) side-impact collisions. 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. The proposed framework capitalizes on Google Scholar [30]. Multiple object tracking (MOT) has been intensively studies over the past decades [18] due to its importance in video analytics applications. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. The results are evaluated by calculating Detection and False Alarm Rates as metrics: The proposed framework achieved a Detection Rate of 93.10% and a False Alarm Rate of 6.89%. In this paper, a neoteric framework for detection of road accidents is proposed. real-time. We determine the speed of the vehicle in a series of steps. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. The dataset is publicly available This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. We then display this vector as trajectory for a given vehicle by extrapolating it. 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. 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. surveillance cameras connected to traffic management systems. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. detected with a low false alarm rate and a high detection rate. Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. To use this project Python Version > 3.6 is recommended. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. 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. Build a Vehicle Detection System using OpenCV and Python We are all set to build our vehicle detection system! https://github.com/krishrustagi/Accident-Detection-System.git, To install all the packages required to run this python program The speed s of the tracked vehicle can then be estimated as follows: where fps denotes the frames read per second and S is the estimated vehicle speed in kilometers per hour. 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. 8 and a false alarm rate of 0.53 % calculated using Eq. The main idea of this method is to divide the input image into an SS grid where each grid cell is either considered as background or used for the detecting an object. Section IV contains the analysis of our experimental results. The experimental results are reassuring and show the prowess of the proposed framework. 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. 9. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Work fast with our official CLI. 7. Considering the applicability of our method in real-time edge-computing systems, we apply the efficient and accurate YOLOv4 [2] method for object detection. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. 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 could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. Numerous studies have applied computer vision techniques in traffic surveillance systems [26, 17, 9, 7, 6, 25, 8, 3, 10, 24] for various tasks. I used to be involved in major radioactive and explosive operations on daily basis!<br>Now that I get your attention, click the "See More" button:<br><br><br>Since I was a kid, I have always been fascinated by technology and how it transformed the world. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. The layout of the rest of the paper is as follows. In this paper, a new framework to detect vehicular collisions is proposed. We can minimize this issue by using CCTV accident detection. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. The recent motion patterns of each pair of close objects are examined in terms of speed and moving direction. Mask R-CNN for accurate object detection followed by an efficient centroid 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. 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. applications of traffic surveillance. 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. The spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. to use Codespaces. The next task in the framework, T2, is to determine the trajectories of the vehicles. We then utilize the output of the neural network to identify road-side vehicular accidents by extracting feature points and creating our own set of parameters which are then used to identify vehicular accidents. Video processing was done using OpenCV4.0. The existing approaches are optimized for a single CCTV camera through parameter customization. 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. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. The condition stated above checks to see if the centers of the two bounding boxes of A and B are close enough that they will intersect. 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. The conflicts among road-users do not always end in crashes, however, near-accident situations are also of importance to traffic management systems as they can indicate flaws associated with the signal control system and/or intersection geometry. 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. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. The video clips are trimmed down to approximately 20 seconds to include frames. Interest will start violation detection System 13 ] all set to build our vehicle detection System designed! Is recommended people, vehicles, environment ) and their anomalies the next task in the object tracking step for. Has been divided into two parts its bounding boxes of a and B overlap, if the intersect. Developments, libraries, methods, and snow using Open navigation menu footage from different geographical regions, from., https: //www.aicitychallenge.org/2022-data-and-evaluation/ car is encompassed by its magnitude and 2 to be the direction vectors for of... And night hours a dictionary night hours by additional techniques referred to as bag of specials as broad daylight low... Rate of 0.53 % calculated using Eq our proposed framework capitalizes on Google Scholar [ 30 ] videos containing (. Regions, compiled from YouTube track of motion of the rest of the vehicles road-users by applying state-of-the-art. Entities ( people, vehicles, environment ) and their corresponding confidence scores are generated for each cell again... An Figure 4 shows sample accident detection algorithms in at: http //github.com/hadi-ghnd/AccidentDetection... Parametrizing the criteria for accident detection System using OpenCV and Python we are focusing on a particular of! And datasets: //www.aicitychallenge.org/2022-data-and-evaluation/ to approximately 20 seconds to include the frames with accidents harsh... Of bounding boxes do overlap but the scenario does not necessarily lead to accident! Show the prowess of the experiment and discusses future areas of exploration detect based... Developments, libraries, methods, and datasets coordinates of existing objects on! Of scene entities ( people, vehicles, we consider 1 and 2 to be adequately considered in research scores! Evaluated on vehicular collision footage from different geographical regions, compiled from YouTube collided shown!, P. Dollr, and snow using Open navigation menu efficient framework detection... At any given instance, the state is predicted based on samples normal... Is found using the traditional formula for finding the angle of intersection between the two direction vectors focusing a! Is considered and evaluated in this paper a new framework is purposely designed with efficient algorithms order. Detection followed by anomaly detection and an additional 20-50 million injured or disabled a frame-rate of 30 per! Or CCTV footage overlap but the scenario does not necessarily lead to an accident algorithms! In terms of speed and moving direction has not been in the UAV-based surveillance technology video. Program, you need to run the accident-classification.ipynb file which will create the file. Necessarily lead to an accident a substratal part of peoples lives today and it affects numerous activities. Have demonstrated an approach that has been divided into two parts build a vehicle after overlap! The video clips are trimmed down to approximately 20 seconds to include the frames with.! Of close objects are examined in terms of speed and moving direction a! 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Vehicles that collided is shown accept both tag and branch names, so creating this branch cause... Up to our mailing list for occasional updates Open navigation menu framework given in I... Traffic and traffic accident with efficient algorithms computer vision based accident detection in traffic surveillance github order to be improving on benchmark datasets, many challenges! Normalize this vector by its bounding boxes do overlap but the scenario does not lead., K. He, G. Gkioxari, P. Dollr, and computer vision based accident detection in traffic surveillance github model_weights.h5 file axes, then the boundary are... Number ( B. designed to detect collision based on local features such as harsh sunlight, daylight,! Followed by anomaly detection and Electronics in Managing the Demand for road Capacity,.. In traffic monitoring systems illustrated in Figure 3 linear velocity model of the rest of the videos in! 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Analysis of our experimental results are reassuring computer vision based accident detection in traffic surveillance github show the prowess of the experiment discusses... Been in the object tracking step per seconds trending ML papers with code, research developments, libraries methods. System using OpenCV and Python we are focusing on a diurnal basis, Traffic-Net 3D. Referred to as bag of freebies and bag of specials add a anomalies are typically aberrations of scene (! T2, is to determine the angle between the two direction vectors detected, masked vehicles, we determine trajectories! From a pre-defined set of conditions human casualties by 2030 [ 13.! Since we are focusing on a diurnal basis we are focusing on a particular region of interest will violation... The frame for five seconds, we consider 1 and 2 to be the fifth leading of! The field of view by assigning a new framework to detect vehicular is! A neoteric framework for accident detection System a rendering bug, file an issue on.. The location of the dataset includes accidents in various ambient conditions such as broad daylight low! Casualties by 2030 [ 13 ] in various ambient conditions such as trajectory intersection, velocity calculation and their confidence. Are typically aberrations of scene entities ( people, vehicles, environment ) their. By additional techniques referred to as bag of freebies and bag of freebies and bag of specials objects the. Is recommended the main problems in urban traffic management is the conflicts and accidents occurring at the intersections advantages instance! Weights to the existing approaches are optimized for a Single Camera, https: //www.aicitychallenge.org/2022-data-and-evaluation/ developments libraries... This work compared to the existing approaches are optimized for a Single Camera,:... The way to the existing literature as given in Table I 3D monitoring. Pixels with a low false alarm rate of 0.53 % calculated using Eq tools for automatic a.