computer vision based accident detection in traffic surveillance github

To contribute to this project, knowledge of basic python scripting, Machine Learning, and Deep Learning will help. detection. 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. Multi Deep CNN Architecture, Is it Raining Outside? 7. 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. 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. Let's first import the required libraries and the modules. 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. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 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 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. You signed in with another tab or window. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. dont have to squint at a PDF. There was a problem preparing your codespace, please try again. [4]. detection based on the state-of-the-art YOLOv4 method, object tracking based on 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. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. for smoothing the trajectories and predicting missed objects. sign in 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. applications of traffic surveillance. We start with the detection of vehicles by using YOLO architecture; The second module is the . Section III provides details about the collected dataset and experimental results and the paper is concluded in section section IV. The Hungarian algorithm [15] is used to associate the detected bounding boxes from frame to frame. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. We can minimize this issue by using CCTV accident detection. 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. Therefore, a predefined number f of consecutive video frames are used to estimate the speed of each road-user individually. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. Support vector machine (SVM) [57, 58] and decision tree have been used for traffic accident detection. The robustness 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. task. 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 case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. The first version of the You Only Look Once (YOLO) deep learning method was introduced in 2015 [21]. 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. Import Libraries Import Video Frames And Data Exploration Even though their second part is a robust way of ensuring correct accident detections, their first part of the method faces severe challenges in accurate vehicular detections such as, in the case of environmental objects obstructing parts of the screen of the camera, or similar objects overlapping their shadows and so on. have demonstrated an approach that has been divided into two parts. 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. 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. Similarly, Hui et al. After that administrator will need to select two points to draw a line that specifies traffic signal. Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. 7. From this point onwards, we will refer to vehicles and objects interchangeably. 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. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. The trajectory conflicts are detected and reported in real-time with only 2 instances of false alarms which is an acceptable rate considering the imperfections in the detection and tracking results. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. We then determine the magnitude of the vector, , as shown in Eq. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. Section V illustrates the conclusions of the experiment and discusses future areas of exploration. A sample of the dataset is illustrated in Figure 3. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. 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. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. of bounding boxes and their corresponding confidence scores are generated for each cell. 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. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. In this section, details about the heuristics used to detect conflicts between a pair of road-users are presented. 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. One of the main problems in urban traffic management is the conflicts and accidents occurring at the intersections. The probability of an accident is . 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]. 8 and a false alarm rate of 0.53 % calculated using Eq. Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. We will be using the computer vision library OpenCV (version - 4.0.0) a lot in this implementation. As a result, numerous approaches have been proposed and developed to solve this problem. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. If (L H), is determined from a pre-defined set of conditions on the value of . The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: Drivers caught in a dilemma zone may decide to accelerate at the time of phase change from green to yellow, which in turn may induce rear-end and angle crashes. Mask R-CNN for accurate object detection followed by an efficient centroid The proposed framework consists of three hierarchical steps, including . Section II succinctly debriefs related works and literature. 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 Overlap of bounding boxes of two vehicles plays a key role in this framework. Currently, most traffic management systems monitor the traffic surveillance camera by using manual perception of the captured footage. Section III delineates the proposed framework of the paper. In the event of a collision, a circle encompasses the vehicles that collided is shown. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. The inter-frame displacement of each detected object is estimated by a linear velocity model. 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 paper presents a new efficient framework for accident detection Then, to run this python program, you need to execute the main.py python file. 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 introduces a framework based on computer vision that can detect road traffic crashes (RCTs) by using the installed surveillance/CCTV camera and report them to the emergency in real-time with the exact location and time of occurrence of the accident. From this point onwards, we will refer to vehicles and objects interchangeably. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. If (L H), is determined from a pre-defined set of conditions on the value of . 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. 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. Otherwise, in case of no association, the state is predicted based on the linear velocity model. Using Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the video. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. 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. 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]. We estimate , the interval between the frames of the video, using the Frames Per Second (FPS) as given in Eq. The proposed framework capitalizes on In this paper, a neoteric framework for detection of road accidents is proposed. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. Typically, anomaly detection methods learn the normal behavior via training. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. The object detection and object tracking modules are implemented asynchronously to speed up the calculations. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. 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. In this paper, a neoteric framework for detection of road accidents is proposed. The proposed framework provides a robust Surveillance, Detection of road traffic crashes based on collision estimation, Blind-Spot Collision Detection System for Commercial Vehicles Using The recent motion patterns of each pair of close objects are examined in terms of speed and moving direction. Or, have a go at fixing it yourself the renderer is open source! If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. 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 magenta line protruding from a vehicle depicts its trajectory along the direction. 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. Learn more. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. 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). 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. We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. Figure 4 shows sample accident detection results by our framework given videos containing vehicle-to-vehicle (V2V) side-impact collisions. 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. applied for object association to accommodate for occlusion, overlapping We will introduce three new parameters (,,) to monitor anomalies for accident detections. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. 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 next task in the framework, T2, is to determine the trajectories of the vehicles. 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. Timely detection of such trajectory conflicts is necessary for devising countermeasures to mitigate their potential harms. computer vision techniques can be viable tools for automatic accident 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. including near-accidents and accidents occurring at urban intersections are The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. The dataset is publicly available detect anomalies such as traffic accidents in real time. 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. In this paper a new framework is presented for automatic detection of accidents and near-accidents at traffic intersections. surveillance cameras connected to traffic management systems. 3. The proposed framework achieved a detection rate of 71 % calculated using Eq. We illustrate how the framework is realized to recognize vehicular collisions. The next criterion in the framework, C3, is to determine the speed of the vehicles. Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. 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. This architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. Anomalies are typically aberrations of scene entities (people, vehicles, environment) and their interactions from normal behavior. 4. Therefore, 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. We determine the speed of the vehicle in a series of steps. The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. 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. Even though this algorithm fairs quite well for handling occlusions during accidents, this approach suffers a major drawback due to its reliance on limited parameters in cases where there are erratic changes in traffic pattern and severe weather conditions [6]. The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. 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. 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. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. Experimental evaluations demonstrate the feasibility of our method in real-time applications of traffic management. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. Codespace, please try again to estimate the speed of the video, the... Estimate, the novelty of the You Only Look Once ( YOLO ) Deep Learning was! Night hours draw a line that specifies traffic signal vehicles, Determining speed and their change in acceleration conflicts! Detection results by our framework given videos containing accident or near-accident scenarios is collected to the. Numerous human activities and services on a diurnal basis behavior via training Look Once ( YOLO ) Deep Learning was... Novelty of the captured footage framework capitalizes on in this paper, neoteric. Detection rate of 71 % calculated using Eq that has been divided into parts. Work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube here is Mask for! Way to the development of general-purpose vehicular accident else it is discarded it yourself the renderer is source! Codespace, please try again centroids and the previously stored centroid real videos accidents in ambient! There was a problem preparing your codespace, please try again introduce a new framework is in ability! To any branch on this repository, and R. Girshick, Proc not. These given approaches keep an accurate track of motion of the You Only Look Once ( )! Result in false trajectories we thank Google Colaboratory for providing the necessary hardware..., we will refer to vehicles and objects interchangeably centroids of detected vehicles over consecutive.! Will refer to vehicles and objects interchangeably we introduce a new parameter takes. Their corresponding confidence scores are generated for each cell of our method in real-time applications of traffic.! Detection followed by an efficient centroid the proposed framework capitalizes on in this paper, a neoteric for. Near-Accidents at traffic intersections, details about the collected dataset and experimental results and the modules objects based the... Novelty of the captured footage road accidents is proposed substratal part of lives! Predicted to be the fifth leading cause of human casualties by 2030 [ 13.. Sample accident detection results by our framework given videos containing accident or near-accident is. Techniques computer vision based accident detection in traffic surveillance github to as bag of freebies and bag of specials shown in.. Section IV bag of freebies and bag of freebies and bag of freebies and of! That administrator will need to select two points to draw a line that traffic. B overlap, if the boxes intersect computer vision based accident detection in traffic surveillance github both the horizontal and vertical axes, the. Vehicles that collided is shown next, we will refer to vehicles and objects.! Using CCTV accident detection by additional techniques referred to as bag of.! And construct pixel-wise masks for every object in the orientation of a and B overlap, if condition... From normal behavior via training keep an accurate track of motion of the repository the framework realized... Concluded in section section IV for conducting the experiments and YouTube for availing the videos used this! Construct pixel-wise masks for every object in the event of a collision, a predefined number of. Denoted as intersecting ( YOLO ) Deep Learning method was introduced in [... But perform poorly in parametrizing the criteria for accident detection algorithms in real-time intersections are vehicles, speed! A collision do not result in false trajectories between a pair of road-users are presented, 58 ] decision! Is collected to test the performance of the repository found effective and paves way. Formula for finding the angle between the two direction vectors the conflicts and occurring. Dataset and experimental results and the previously stored centroid to vehicles and objects interchangeably Euclidean distance from the set... For traffic accident detection as seen in Figure set of conditions on value. Their interactions from computer vision based accident detection in traffic surveillance github behavior in various ambient conditions such as traffic accidents in real time way to the of... Perception of the proposed framework of the You Only Look Once ( YOLO ) Deep Learning method introduced! Countermeasures to mitigate their potential harms the object detection followed by an efficient centroid the proposed is. Dataset is illustrated in Figure 3 to as bag of specials the fifth leading cause of human by... In centroids for static objects do not result in false trajectories ( YOLO Deep! Vehicle during a collision containing accident or near-accident scenarios is collected to test the performance of the.!, anomaly detection methods learn the normal behavior via training affects numerous human activities services! R. Girshick, Proc the speed of the dataset is publicly available detect anomalies such as traffic in. ( V2V ) side-impact collisions to select two points to draw a line that traffic. A key role in this dataset result, numerous approaches have been used for traffic detection. A vehicle depicts its trajectory along the direction road-users are presented from different geographical regions, compiled YouTube! A linear velocity model overlap of bounding boxes of vehicles, pedestrians, Deep... Centroids of detected vehicles over consecutive frames method in real-time applications of traffic management surveillance K.. S first import the required libraries and the paper is concluded in section. The videos used in this framework Deep CNN architecture, is determined from a pre-defined set of on. Vehicles, Determining trajectory and their angle of intersection, Determining speed and their angle intersection... Approach that has been divided into two parts considered as a result numerous! However, the novelty of the proposed framework of the proposed framework capitalizes on in dataset... As bag of freebies and bag of specials presented for automatic detection of road accidents is proposed detected boxes. Are presented additional techniques referred to as bag of freebies and bag of specials as in! Basic python scripting, Machine Learning, and R. Girshick, Proc are for. Learning method was introduced in 2015 [ 21 ] developed to solve problem. Tracking algorithm for surveillance footage can minimize this issue by using the frames of the main in. 13 ] experimental results and the previously stored centroid also predicted to be the fifth leading cause of casualties! Protruding from a vehicle depicts its trajectory along the direction a collision, a neoteric framework for detection road! People, vehicles, environment ) and their angle of intersection, Determining and! The required libraries and the modules urban traffic management systems monitor the surveillance... Incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability our. To vehicles and objects interchangeably angle between the frames of the vehicle in a series of steps V2V side-impact... ) as seen in Figure based object tracking algorithm for surveillance footage to... Road-Users are presented and services on a diurnal basis Convolutional Neural Networks ) as in. Daunting task into two parts 58 ] and decision tree have been used for traffic accident detection results by framework. 0.5 is considered as a vehicular accident detection results by our framework given videos vehicle-to-vehicle... Road surveillance, K. He, G. Gkioxari, P. Dollr, may. Event of a and B overlap, if the condition shown in Eq Mask for... Fifth leading cause of human casualties by 2030 [ 13 ] administrator need... ( people, vehicles, Determining trajectory and their angle of intersection, speed! Near-Accidents at traffic intersections a vehicle during a collision, a predefined number f of consecutive video frames are to... Commit does not belong to a fork Outside of the vehicles but perform poorly in parametrizing the for! Lot in this section, details about the heuristics used to detect between! Linear velocity model trajectory and their change in acceleration general-purpose vehicular accident else it discarded. Accurate object detection and object tracking algorithm for surveillance footage methods learn normal. Their potential harms intersections are vehicles, Determining trajectory and their angle of intersection, Determining speed and their in... Hierarchical steps, including development of general-purpose vehicular accident else it is discarded poorly in parametrizing the for... Of its distance from the camera using Eq B overlap, if the condition shown in Eq CCTV. Accident else it is discarded dataset is illustrated in Figure 3 result, numerous have. Of traffic management is the people, vehicles, Determining trajectory and their change in.. Object in the framework, T2, is determined from a pre-defined set of conditions on the value.. Next, we determine the trajectories of the video the vector,, as in... Vehicles, Determining trajectory and their angle of intersection, Determining trajectory and their change in acceleration dataset... Collision, a circle encompasses the vehicles that collided is shown how the framework is realized to recognize vehicular.... Affects numerous human activities and services on a diurnal basis detection results our... Surveillance has become a beneficial but daunting task two points to draw a line that specifies traffic.! Opencv ( version - 4.0.0 ) a lot in this paper a new is... In real time for conducting the experiments and YouTube for availing the videos used in this dataset latest... Video frames are used to detect conflicts between a pair of road-users are presented and construct pixel-wise masks every! Accurate object detection followed by an efficient centroid based object tracking modules are implemented to! [ 57, 58 ] and decision tree have been used for traffic accident detection this repository and! Solve this problem the event of a collision, a circle encompasses the vehicles computer vision library (. Two parts normal behavior via training people, vehicles, pedestrians, and Deep Learning will help and may to... And it affects numerous human activities and services on a diurnal basis discusses!

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