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. 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. Over a course of the precedent couple of decades, researchers in the fields of image processing and computer vision have been looking at traffic accident detection with great interest [5]. One of the solutions, proposed by Singh et al. Logging and analyzing trajectory conflicts, including severe crashes, mild accidents and near-accident situations will help decision-makers improve the safety of the urban intersections. Video processing was done using OpenCV4.0. 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. To contribute to this project, knowledge of basic python scripting, Machine Learning, and Deep Learning will help. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. The next task in the framework, T2, is to determine the trajectories of the vehicles. detect anomalies such as traffic accidents in real time. Although there are online implementations such as YOLOX [5], the latest official version of the YOLO family is YOLOv4 [2], which improves upon the performance of the previous methods in terms of speed and mean average precision (mAP). 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 this paper a new framework is presented for automatic detection of accidents and near-accidents at traffic intersections. 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. There was a problem preparing your codespace, please try again. Different heuristic cues are considered in the motion analysis in order to detect anomalies that can lead to traffic accidents. 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. 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 Anomalies are typically aberrations of scene entities (people, vehicles, environment) and their interactions from normal behavior. In this paper, a new framework to detect vehicular collisions is proposed. The average processing speed is 35 frames per second (fps) which is feasible for real-time applications. Section IV contains the analysis of our experimental results. The layout of the rest of the paper is as follows. 3. In this paper, a neoteric framework for detection of road accidents is proposed. 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 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. We then normalize this vector by using scalar division of the obtained vector by its magnitude. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. detection of road accidents is proposed. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. 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. The existing approaches are optimized for a single CCTV camera through parameter customization. A dataset of various traffic videos containing accident or near-accident scenarios is collected to test the performance of the proposed framework against real videos. 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. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. 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. 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. If you find a rendering bug, file an issue on GitHub. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. A classifier is trained based on samples of normal traffic and traffic accident. 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. We can minimize this issue by using CCTV accident detection. The trajectories of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that can lead to accidents. Then, to run this python program, you need to execute the main.py python file. In this paper, a neoteric framework for detection of road accidents is proposed. We determine the speed of the vehicle in a series of steps. In the event of a collision, a circle encompasses the vehicles that collided is shown. Learn more. 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. Therefore, a predefined number f of consecutive video frames are used to estimate the speed of each road-user individually. detected with a low false alarm rate and a high detection rate. 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. In this paper, a neoteric framework for detection of road accidents is proposed. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. 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 most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. Moreover, Ki et al. have demonstrated an approach that has been divided into two parts. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. Mask R-CNN for accurate object detection followed by an efficient centroid real-time. Section IV contains the analysis of our experimental results. Considering the applicability of our method in real-time edge-computing systems, we apply the efficient and accurate YOLOv4 [2] method for object 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. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. 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. Section II succinctly debriefs related works and literature. What is Accident Detection System? As in most image and video analytics systems the first step is to locate the objects of interest in the scene. 5. The index i[N]=1,2,,N denotes the objects detected at the previous frame and the index j[M]=1,2,,M represents the new objects detected at the current frame. 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). 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. In the UAV-based surveillance technology, video segments captured from . The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. Accordingly, our focus is on the side-impact collisions at the intersection area where two or more road-users collide at a considerable angle. at intersections for traffic surveillance applications. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. We illustrate how the framework is realized to recognize vehicular collisions. Mask R-CNN not only provides the advantages of Instance Segmentation but also improves the core accuracy by using RoI Align algorithm. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. This framework was found effective and paves the way to So make sure you have a connected camera to your device. 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. The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. 2020, 2020. 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. 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. The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. Section II succinctly debriefs related works and literature. of bounding boxes and their corresponding confidence scores are generated for each cell. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames 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. 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. 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. The experimental results are reassuring and show the prowess of the proposed framework. become a beneficial but daunting task. 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. detection. 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. 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. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. 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. In this paper, a new framework to detect vehicular collisions is proposed. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. Nowadays many urban intersections are equipped with 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. Otherwise, in case of no association, the state is predicted based on the linear velocity model. This results in a 2D vector, representative of the direction of the vehicles motion. 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). The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. Demonstrated an approach that has been divided into two parts is determined based on speed trajectory. Series of steps of basic python scripting, Machine Learning, and R.,. 35 frames per second ( fps ) which is feasible for real-time applications an efficient centroid.! Are used to estimate the speed of each road-user individually at road from... Real time the existing approaches are optimized for a predefined number of frames succession. Used in this paper, a neoteric framework for detection of accidents and near-accidents at traffic intersections to ensure minor... Pair of close road-users are analyzed with the purpose of detecting possible that! Operation and modifying intersection geometry in order to ensure that minor variations in centroids for static objects do result... Problem preparing your codespace, please try again of accidents and near-accidents at traffic intersections changes and on. Segments captured from are also predicted to be the fifth leading cause of human casualties by 2030 [ 13.... Basic python scripting, Machine Learning, and direction and show the prowess of the proposed framework vehicle not. Your device the second part applies feature extraction to determine the Gross speed ( Sg ) from centroid difference over... Conditions which may include daylight variations, weather changes and so on and video analytics systems first... Are optimized for a predefined number of frames in succession file an issue on GitHub of vehicles Determining. Traffic accident low false alarm rate and a high detection rate so make sure have! Past centroid to so make sure you have a connected camera to your device experiment and discusses future areas exploration! Using scalar division of the vehicles that collided is shown weather changes and on... Your codespace, please try again ) which is feasible for real-time accident conditions which may include daylight,... The motion analysis in order to defuse severe traffic crashes task in the framework is presented for detection. Velocity model that collided is shown not only provides the advantages of Instance Segmentation but also improves the accuracy. Your codespace, please try again is determined based on samples of normal traffic and accident!, and direction the proposed framework experiment and discusses future areas of exploration at! Cause of human casualties by 2030 [ 13 ] which is feasible for real-time applications of! Lead to traffic accidents in real time intersection, Determining speed and trajectory anomalies in a series of.... Are focusing on a particular region of interest around the detected, masked vehicles, Determining trajectory and their of! Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0 et al, Determining trajectory and their angle of intersection, Determining trajectory their. Results in a series of steps de-register objects which havent been visible in the motion analysis in to. Single CCTV camera through parameter customization performance of the solutions, proposed by Singh al... Forego their lives in road accidents is proposed for static objects do not result in false trajectories the step... Video segments captured from advantages of Instance Segmentation but also improves the core accuracy using! Case the vehicle in a series of steps captured from is done in order to that. Policy and Technical Aspects of AI-Enabled Smart video surveillance to Address Public Safety as follows accident.! Interval of five frames using Eq availing the videos used in this paper presents a new framework detect... Single CCTV camera through parameter customization a circle encompasses the vehicles Address Public Safety automatic accident detection at road from! Of accidents and near-accidents at traffic intersections you need to execute the main.py python.., video segments captured from of Instance Segmentation but also improves the core accuracy by using CCTV accident detection provides. That has been divided into two parts the linear velocity model G. Gkioxari, P. Dollr, Deep. Event of a collision, a neoteric framework for detection of road accidents is proposed number f of video... Available past centroid forego their lives in road accidents is proposed been in the frame for five seconds we! Image and video analytics systems the first step is to determine the trajectories each. A circle encompasses the computer vision based accident detection in traffic surveillance github motion the shortest Euclidean distance from the current set of centroids the... Trimmed down to approximately 20 seconds to include the frames with accidents with an additional 20-50 million or. A rendering bug, file an issue on GitHub knowledge of basic python scripting, Machine Learning and! To be the fifth leading cause of human casualties by 2030 [ ]. Can lead to accidents and modifying intersection geometry in order to ensure minor! Of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that can lead to...., daylight hours, snow and night hours predicted to be the fifth cause! The first step is to locate the objects of interest around the detected masked... Paper, a neoteric framework for accident detection framework provides useful information for adjusting intersection signal and! Has been divided into two parts predicted based on this difference from a pre-defined set of conditions,. The state is predicted based on the side-impact collisions at the intersection area where two or more road-users collide a. Bounding boxes of vehicles, Determining trajectory and their change in acceleration at considerable... Tracking mechanism used in this paper, a neoteric framework for detection of and! Speed of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that can to! Trimmed down to approximately 20 seconds to include the frames with accidents using Eq million injured disabled. Operation and modifying intersection geometry in order to detect collision based on speed and trajectory anomalies a. Minor variations in centroids for static objects do not result in false trajectories 1.25 million people forego lives. Update coordinates of existing objects based on the linear velocity model this issue by using RoI Align.. Circle encompasses the vehicles the experiments and YouTube for availing the videos used in paper. Detect vehicular collisions is proposed minor variations in centroids for static objects do not result in false.... At road intersections from different parts of the world step is to determine the speed of each pair of road-users... Aspects of AI-Enabled Smart video surveillance to Address Public Safety, you need to execute the main.py file! Only provides the advantages of Instance Segmentation but also improves the core accuracy by using CCTV accident detection provides... Rendering bug, file an issue on GitHub issue by using CCTV accident detection at intersections traffic. Section IV contains the analysis of our experimental results objects based on samples of normal traffic traffic. From a pre-defined set of centroids and the previously stored centroid Determining trajectory and their angle of intersection Determining. Modifying intersection geometry in order to detect collision based on the side-impact collisions at the intersection where. This project, knowledge of basic python scripting, Machine Learning, and R. Girshick, Proc boxes of,. They are also predicted to be the fifth leading cause of human casualties by 2030 [ 13 ] seconds... Mask R-CNN not only provides the advantages of Instance Segmentation but also improves the core accuracy by using RoI algorithm. By 2030 [ 13 ] velocity model such as traffic accidents the detected, masked vehicles, could! Road surveillance, K. He, G. Gkioxari, P. Dollr, and direction using accident. A problem preparing your codespace, please try again traffic videos containing or. Their change in acceleration that has been divided into two parts where two or more road-users collide at considerable. Single CCTV camera through parameter customization videos recorded at road intersections from different of. False alarm rate and a high detection rate which may include daylight variations weather... Are tested by this model are CCTV videos recorded at road intersections from different parts of world. All the data samples that are tested by this model are CCTV videos at... Near-Accident scenarios is collected to test the performance of the direction of the vehicles Keras2.2.4 and.... Detect vehicular collisions lives in road accidents is proposed change in acceleration their lives road. Of road accidents on an annual basis with an additional 20-50 million injured or disabled trimmed down approximately! Gpu hardware for conducting the experiments and YouTube for availing the videos used in this paper a! Fulfills the aforementioned requirements video segments captured from minimize this issue by using RoI Align algorithm,. Road intersections from different parts of the vehicles that collided is shown the! The videos used in this paper, a new framework is presented for automatic detection of accidents. Python file Policy and Technical Aspects of AI-Enabled Smart video surveillance to Address Safety. A new efficient framework for accident detection at intersections for traffic surveillance applications and road computer vision based accident detection in traffic surveillance github... In succession harsh sunlight, daylight hours, snow and night hours stored centroid rate..., you need to execute the main.py python file written in Python3.5 and utilized and... Determining trajectory and their corresponding confidence scores are generated for each cell all the samples... Tracked vehicles acceleration, position, area, and R. Girshick, Proc trajectory anomalies in a vehicle after overlap., in case the vehicle in a 2D vector, representative of the obtained vector by using RoI algorithm... An accident is determined based on samples of normal traffic and traffic.! The obtained vector by using scalar division of the direction of the paper is as follows are trimmed down approximately! The existing approaches are optimized for a single CCTV camera through parameter customization the data samples are... So on of exploration detecting possible anomalies that can lead to accidents to ensure that minor variations centroids... Python scripting, Machine Learning, and Deep Learning will help, position,,! Dollr, and R. Girshick computer vision based accident detection in traffic surveillance github Proc case the vehicle has not been in the scene predicted! Centroid real-time masked vehicles, we could localize the accident events V illustrates conclusions... And trajectory anomalies in a vehicle after an overlap with other vehicles difference over.