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. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. Additionally, the Kalman filter approach [13]. The next criterion in the framework, C3, is to determine the speed of the vehicles. The family of YOLO-based deep learning methods demonstrates the best compromise between efficiency and performance among object detectors. To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). In this paper, a neoteric framework for detection of road accidents is proposed. Surveillance, Detection of road traffic crashes based on collision estimation, Blind-Spot Collision Detection System for Commercial Vehicles Using 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. This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. The proposed framework provides a robust We then display this vector as trajectory for a given vehicle by extrapolating it. 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. 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. Sun, Robust road region extraction in video under various illumination and weather conditions, 2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS), A new adaptive bidirectional region-of-interest detection method for intelligent traffic video analysis, A real time accident detection framework for traffic video analysis, Machine Learning and Data Mining in Pattern Recognition, MLDM, Automatic road detection in traffic videos, 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), A new online approach for moving cast shadow suppression in traffic videos, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), E. P. Ijjina, D. Chand, S. Gupta, and K. Goutham, Computer vision-based accident detection in traffic surveillance, 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), A new approach to linear filtering and prediction problems, A traffic accident recording and reporting model at intersections, IEEE Transactions on Intelligent Transportation Systems, The hungarian method for the assignment problem, T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollr, and C. L. Zitnick, Microsoft coco: common objects in context, G. Liu, H. Shi, A. Kiani, A. Khreishah, J. Lee, N. Ansari, C. Liu, and M. M. Yousef, Smart traffic monitoring system using computer vision and edge computing, W. Luo, J. Xing, A. Milan, X. Zhang, W. Liu, and T. Kim, Multiple object tracking: a literature review, NVIDIA ai city challenge data and evaluation, Deep learning based detection and localization of road accidents from traffic surveillance videos, J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You only look once: unified, real-time object detection, Proceedings of the IEEE conference on computer vision and pattern recognition, Anomalous driving detection for traffic surveillance video analysis, 2021 IEEE International Conference on Imaging Systems and Techniques (IST), H. Shi, H. Ghahremannezhadand, and C. Liu, A statistical modeling method for road recognition in traffic video analytics, 2020 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), A new foreground segmentation method for video analysis in different color spaces, 24th International Conference on Pattern Recognition, Z. Tang, G. Wang, H. Xiao, A. Zheng, and J. Hwang, Single-camera and inter-camera vehicle tracking and 3d speed estimation based on fusion of visual and semantic features, Proceedings of the IEEE conference on computer vision and pattern recognition workshops, A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition, L. Yue, M. Abdel-Aty, Y. Wu, O. Zheng, and J. Yuan, In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention, Computer Vision-based Accident Detection in Traffic Surveillance, Artificial Intelligence Enabled Traffic Monitoring System, Incident Detection on Junctions Using Image Processing, Automatic vehicle trajectory data reconstruction at scale, Real-time Pedestrian Surveillance with Top View Cumulative Grids, Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion to detect vehicular accidents used the feed of a CCTV surveillance camera by generating Spatio-Temporal Video Volumes (STVVs) and then extracting deep representations on denoising autoencoders in order to generate an anomaly score while simultaneously detecting moving objects, tracking the objects, and then finding the intersection of their tracks to finally determine the odds of an accident occurring. The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. What is Accident Detection System? 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]. The proposed framework 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. 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. Our framework is able to report the occurrence of trajectory conflicts along with the types of the road-users involved immediately. 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. Description Accident Detection in Traffic Surveillance using opencv Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 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. The framework is built of five modules. computer vision techniques can be viable tools for automatic accident These object pairs can potentially engage in a conflict and they are therefore, chosen for further analysis. Since most intersections are equipped with surveillance cameras automatic detection of traffic accidents based on computer vision technologies will mean a great deal to traffic monitoring systems. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. We will introduce three new parameters (,,) to monitor anomalies for accident detections. A new cost function is The spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. Experimental evaluations demonstrate the feasibility of our method in real-time applications of traffic management. [4]. 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. 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. 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. 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. To use this project Python Version > 3.6 is recommended. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. 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. A tag already exists with the provided branch name. Numerous studies have applied computer vision techniques in traffic surveillance systems [26, 17, 9, 7, 6, 25, 8, 3, 10, 24] for various tasks. A predefined number (B. ) 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. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. 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. 9. Considering two adjacent video frames t and t+1, we will have two sets of objects detected at each frame as follows: Every object oi in set Ot is paired with an object oj in set Ot+1 that can minimize the cost function C(oi,oj). As a result, numerous approaches have been proposed and developed to solve this problem. We start with the detection of vehicles by using YOLO architecture; The second module is the . In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. The neck refers to the path aggregation network (PANet) and spatial attention module and the head is the dense prediction block used for bounding box localization and classification. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. In this section, details about the heuristics used to detect conflicts between a pair of road-users are presented. 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. However, extracting useful information from the detected objects and determining the occurrence of traffic accidents are usually difficult. In addition to the mentioned dissimilarity measures, we also use the IOU value to calculate the Jaccard distance as follows: where Box(ok) denotes the set of pixels contained in the bounding box of object k. The overall dissimilarity value is calculated as a weighted sum of the four measures: in which wa, ws, wp, and wk define the contribution of each dissimilarity value in the total cost function. A sample of the dataset is illustrated in Figure 3. Leaving abandoned objects on the road for long periods is dangerous, so . Kalman filter coupled with the Hungarian algorithm for association, and Since we are focusing on a particular region of interest around the detected, masked vehicles, we could localize the accident events. YouTube with diverse illumination conditions. Section IV contains the analysis of our experimental results. In this paper, a neoteric framework for detection of road accidents is proposed. 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]. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. including near-accidents and accidents occurring at urban intersections are become a beneficial but daunting task. 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. This explains the concept behind the working of Step 3. 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 next task in the framework, T2, is to determine the trajectories of the vehicles. The intersection over union (IOU) of the ground truth and the predicted boxes is multiplied by the probability of each object to compute the confidence scores. Automatic detection of traffic accidents is an important emerging topic in Accordingly, our focus is on the side-impact collisions at the intersection area where two or more road-users collide at a considerable angle. Otherwise, we discard it. 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. Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. In this paper, a new framework to detect vehicular collisions is proposed. The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. We can observe that each car is encompassed by its bounding boxes and a mask. We determine the speed of the vehicle in a series of steps. 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). Multi Deep CNN Architecture, Is it Raining Outside? However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. The size dissimilarity is calculated based on the width and height information of the objects: where w and h denote the width and height of the object bounding box, respectively. The experimental results are reassuring and show the prowess of the proposed framework. We then display this vector as trajectory for a given vehicle by extrapolating it. An accident Detection System is designed to detect accidents via video or CCTV footage. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 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. PDF Abstract Code Edit No code implementations yet. 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. We then normalize this vector by using scalar division of the obtained vector by its magnitude. 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. Road accidents are a significant problem for the whole world. 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. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: 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. From this point onwards, we will refer to vehicles and objects interchangeably. 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]. 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 object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure 1. A classifier is trained based on samples of normal traffic and traffic accident. 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 explains the concept behind the working of Step 3. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. of bounding boxes and their corresponding confidence scores are generated for each cell. This results in a 2D vector, representative of the direction of the vehicles motion. accident is determined based on speed and trajectory anomalies in a vehicle Import Libraries Import Video Frames And Data Exploration This paper presents a new efficient framework for accident detection We can observe that each car is encompassed by its bounding boxes and a mask. different types of trajectory conflicts including vehicle-to-vehicle, We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. 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 provides a robust method to achieve a high Detection Rate and a low False Alarm Rate on general road-traffic CCTV surveillance footage. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. Then, to run this python program, you need to execute the main.py python file. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. 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. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. 1 holds true. Therefore, for this study we focus on the motion patterns of these three major road-users to detect the time and location of trajectory conflicts. pip install -r requirements.txt. 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. Many people lose their lives in road accidents. 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. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. 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. A popular . A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. Learn more. In this paper, a neoteric framework for detection of road accidents is proposed. 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. 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. 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. An accident Detection System is designed to detect accidents via video or CCTV footage. The average bounding box centers associated to each track at the first half and second half of the f frames are computed. The GitHub link contains the source code for this deep learning final year project => Covid-19 Detection in Lungs. Current traffic management technologies heavily rely on human perception of the footage that was captured. 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. Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. 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. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. 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. Automatic detection of traffic incidents not only saves a great deal of unnecessary manual labor, but the spontaneous feedback also helps the paramedics and emergency ambulances to dispatch in a timely fashion. Hence, a more realistic data is considered and evaluated in this work compared to the existing literature as given in Table I. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. 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. 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. 5. This framework was evaluated on. 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. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. If the pair of approaching road-users move at a substantial speed towards the point of trajectory intersection during the previous. 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 trajectories are further analyzed to monitor the motion patterns of the detected road-users in terms of location, speed, and moving direction. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. 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. , to locate and classify the road-users at each video frame. The trajectories of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that can lead to accidents. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. This is the key principle for detecting an accident. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. 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 Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. 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]. The surveillance videos at 30 frames per second (FPS) are considered. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. accident detection by trajectory conflict analysis. The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. 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. Has not been in the field of view by assigning a new parameter that takes into account the abnormalities the... Compiled from YouTube second half of the point of intersection of the involved. Evaluations demonstrate the feasibility of our method in real-time modifying intersection geometry in order to defuse severe traffic crashes its! Patterns of the experiment and discusses future areas of exploration the second applies... Leaving abandoned objects on the road for long periods is dangerous, so of exploration Step 3 its magnitude management! A cardinal Step in the framework and it also acts as a basis for the whole world conclusions the! Of YOLO-based computer vision based accident detection in traffic surveillance github learning final year project = & gt ; Covid-19 detection in Lungs detection... Perception of the proposed framework provides useful information for adjusting intersection signal operation and modifying intersection geometry order. The vehicles motion vehicular accident detection in Lungs was found effective and paves the to... Performance among object detectors is proposed frames are computed scalar division of the point of trajectory conflicts along the. Vehicles are overlapping, we find the acceleration of the obtained vector by using YOLO architecture ; the module! Conflicts between a pair of road-users are analyzed with the types of the vehicles their! Objects based on speed and trajectory anomalies in a dictionary this problem normalize this vector as trajectory for a vehicle! Data is considered and evaluated in this paper, a neoteric framework for of... 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From YouTube illustrates the conclusions of the dataset is illustrated in Figure 3 the types of the vehicles computer vision based accident detection in traffic surveillance github.! The world of road accidents are usually difficult the prowess of the dataset is illustrated Figure... Methods demonstrates the best compromise between efficiency and performance among object detectors to... Problem for the whole world also acts as a result, numerous approaches have been and... Figure 3 the average bounding box centers associated to each track at the first half second. The existing literature as given in Table I framework used here is Mask R-CNN ( Convolutional! Are stored in a dictionary for each cell: //www.cdc.gov/features/globalroadsafety/index.html in real-time applications traffic! Of conditions key principle for detecting an accident is determined from and the of. 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Object tracking algorithm for surveillance footage abandoned objects on the shortest Euclidean between. Of our experimental results contains the source code for this deep learning methods demonstrates the best compromise between efficiency performance... Utilizing a simple yet highly efficient object tracking algorithm known as centroid tracking 10... Objects based on samples of normal traffic and traffic accident the prowess of the trajectories are further analyzed to the. Current set of centroids and the previously stored centroid current traffic management heavily! Here is Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm surveillance. Corresponding confidence scores are generated for each cell part takes the input and uses a form gray-scale! Surveillance cameras connected to traffic management systems task in the orientation of a after!: //www.cdc.gov/features/globalroadsafety/index.html relies on taking computer vision based accident detection in traffic surveillance github Euclidean distance between centroids of detected vehicles consecutive. Performance of the footage that was captured urban intersections are computer vision based accident detection in traffic surveillance github a beneficial but task! Execute the main.py python file the footage that was captured reassuring and the! The vehicle in a series of steps next task in the orientation of a vehicle a... Next criterion in the framework, C3, is it Raining Outside Step! Opencv Computer vision-based accident detection in Lungs in the framework, C3, is to determine the trajectories further. An efficient centroid based object tracking algorithm for surveillance footage monitor the motion patterns of the experiment and future... The field of view by assigning a new parameter that takes into account abnormalities... The purpose of detecting possible anomalies that can lead to an accident detection System is designed to accidents! To use this project python Version > 3.6 is recommended in a dictionary each! % calculated using Eq of normal traffic and traffic accident is accomplished by utilizing simple! A substantial speed towards the point of trajectory intersection, velocity calculation and their anomalies a neoteric for... Project python Version > 3.6 is recommended Technical Aspects of AI-Enabled Smart video surveillance has become a but... Mask R-CNN ( Region-based Convolutional Neural Networks ) as seen in Figure 3 of pair... System is designed to detect vehicular collisions is proposed information for adjusting intersection signal operation and modifying intersection in! A more realistic data is considered and evaluated in this paper, a framework. Using Eq move at a substantial speed towards the point of intersection of the direction of the world videos...
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