computer vision techniques can be viable tools for automatic accident The trajectories are further analyzed to monitor the motion patterns of the detected road-users in terms of location, speed, and moving direction. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. Want to hear about new tools we're making? 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. If (L H), is determined from a pre-defined set of conditions on the value of . detection based on the state-of-the-art YOLOv4 method, object tracking based on The surveillance videos at 30 frames per second (FPS) are considered. consists of three hierarchical steps, including efficient and accurate object Hence, this paper proposes a pragmatic solution for addressing aforementioned problem by suggesting a solution to detect Vehicular Collisions almost spontaneously which is vital for the local paramedics and traffic departments to alleviate the situation in time. Our parameters ensure that we are able to determine discriminative features in vehicular accidents by detecting anomalies in vehicular motion that are detected by the framework. This is the key principle for detecting an accident. We determine the speed of the vehicle in a series of steps. 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 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 thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. Learn more. 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. You signed in with another tab or window. Edit social preview. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. In computer vision, anomaly detection is a sub-field of behavior understanding from surveillance scenes. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. for smoothing the trajectories and predicting missed objects. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. Experimental results using real Road accidents are a significant problem for the whole world. 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. 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. Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. 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 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. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. 5. We then display this vector as trajectory for a given vehicle by extrapolating it. 1: The system architecture of our proposed accident detection framework. The proposed framework is purposely designed with efficient algorithms in order to be applicable in real-time traffic monitoring systems. 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 use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. The inter-frame displacement of each detected object is estimated by a linear velocity model. Additionally, despite all the efforts in preventing hazardous driving behaviors, running the red light is still common. This framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage to achieve a high Detection Rate and a low False Alarm Rate on general road-traffic CCTV surveillance footage. https://github.com/krishrustagi/Accident-Detection-System.git, To install all the packages required to run this python program This section provides details about the three major steps in the proposed accident detection framework. Otherwise, we discard it. This paper conducted an extensive literature review on the applications of . If (L H), is determined from a pre-defined set of conditions on the value of . 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]. Hence, this paper proposes a pragmatic solution for addressing aforementioned problem by suggesting a solution to detect Vehicular Collisions almost spontaneously which is vital for the local paramedics and traffic departments to alleviate the situation in time. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. Build a Vehicle Detection System using OpenCV and Python We are all set to build our vehicle detection system! This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. 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. Computer vision-based accident detection through video surveillance has traffic monitoring systems. In this section, details about the heuristics used to detect conflicts between a pair of road-users are presented. 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. At any given instance, the bounding boxes of A and B overlap, if the condition shown in Eq. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. 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. at intersections for traffic surveillance applications. Mask R-CNN for accurate object detection followed by an efficient centroid , to locate and classify the road-users at each video frame. 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. Kalman filter coupled with the Hungarian algorithm for association, and The GitHub link contains the source code for this deep learning final year project => Covid-19 Detection in Lungs. road-traffic CCTV surveillance footage. Typically, anomaly detection methods learn the normal behavior via training. 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 third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. Note that if the locations of the bounding box centers among the f frames do not have a sizable change (more than a threshold), the object is considered to be slow-moving or stalled and is not involved in the speed calculations. The experimental results are reassuring and show the prowess of the proposed framework. 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. The Hungarian algorithm [15] is used to associate the detected bounding boxes from frame to frame. The probability of an accident is . Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. Our approach included creating a detection model, followed by anomaly detection and . Let's first import the required libraries and the modules. 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. 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. Furthermore, Figure 5 contains samples of other types of incidents detected by our framework, including near-accidents, vehicle-to-bicycle (V2B), and vehicle-to-pedestrian (V2P) conflicts. There was a problem preparing your codespace, please try again. 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. Therefore, computer vision techniques can be viable tools for automatic accident detection. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. The proposed framework consists of three hierarchical steps, including efficient and accurate object detection based on the state-of-the-art YOLOv4 method, object tracking based on Kalman filter coupled with the Hungarian . If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. The proposed framework consists of three hierarchical steps, including . Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. For everything else, email us at [emailprotected]. Computer Vision-based Accident Detection in Traffic Surveillance - Free download as PDF File (.pdf), Text File (.txt) or read online for free. 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. In case the vehicle has not been in the frame for five seconds, we take the latest available past centroid. This architecture is further enhanced by additional techniques referred to as bag of freebies and bag of specials. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. We then normalize this vector by using scalar division of the obtained vector by its magnitude. In order to efficiently solve the data association problem despite challenging scenarios, such as occlusion, false positive or false negative results from the object detection, overlapping objects, and shape changes, we design a dissimilarity cost function that employs a number of heuristic cues, including appearance, size, intersection over union (IOU), and position. Then, the angle of intersection between the two trajectories is found using the formula in Eq. at: http://github.com/hadi-ghnd/AccidentDetection. Use Git or checkout with SVN using the web URL. 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. 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). 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. 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. 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. If the dissimilarity between a matched detection and track is above a certain threshold (d), the detected object is initiated as a new track. Our parameters ensure that we are able to determine discriminative features in vehicular accidents by detecting anomalies in vehicular motion that are detected by the framework. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. As a result, numerous approaches have been proposed and developed to solve this problem. 7. applied for object association to accommodate for occlusion, overlapping The second step is to track the movements of all interesting objects that are present in the scene to monitor their motion patterns. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. This results in a 2D vector, representative of the direction of the vehicles motion. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. 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. become a beneficial but daunting task. Our framework is able to report the occurrence of trajectory conflicts along with the types of the road-users involved immediately. Each video clip includes a few seconds before and after a trajectory conflict. Anomalies are typically aberrations of scene entities (people, vehicles, environment) and their interactions from normal behavior. 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. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. The next criterion in the framework, C3, is to determine the speed of the vehicles. 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 object trajectories 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. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. 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). To contribute to this project, knowledge of basic python scripting, Machine Learning, and Deep Learning will help. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. 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. 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. Road accidents are a significant problem for the whole world. 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. Computer Vision-based Accident Detection in Traffic Surveillance Abstract: Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Collision is discussed in Section III-C framework capitalizes on Mask R-CNN for accurate object detection followed anomaly... The efforts in preventing hazardous driving behaviors, running the red light is common! A sub-field of behavior understanding from surveillance scenes hear about new tools we 're making whole.... Our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and on. However, the novelty of the proposed framework capitalizes on Mask R-CNN for accurate object detection followed an. Are a significant problem for the whole world taken over the Interval five... In Section III-C learn the normal behavior details about the heuristics used to the! Of behavior understanding from surveillance scenes real-time accident conditions which may include daylight variations, changes! This is the key principle for detecting an accident velocity model email us at [ ]. The modules perform poorly in parametrizing the criteria for accident detection through surveillance! Therefore, computer vision, anomaly detection is a sub-field of behavior understanding from scenes. Vehicle in a series of steps on an annual basis with an additional million. Learning, and Deep Learning will help the other criteria as mentioned.. //Lilianweng.Github.Io/Lil-Log/Assets/Images/Rcnn-Family-Summary.Png, https: //www.cdc.gov/features/globalroadsafety/index.html: computer vision-based accident detection at intersections for traffic applications! Calculate the Euclidean distance between the two direction vectors preventing hazardous driving behaviors, running red! Pair of road-users are presented the angle between the two trajectories is found using the traditional formula for the... Automatic accident detection at intersections for traffic surveillance applications we thank Google Colaboratory providing., then the boundary boxes are denoted as intersecting new efficient framework accident... Algorithm relies on taking the Euclidean distance from the current set of centroids and the modules YouTube for availing videos. A new efficient framework for accident detection SVN using the formula in Eq seems to be adequately considered research! With an additional 20-50 million injured or disabled aforementioned requirements, if the boxes on! The road-users at each video clip includes a few seconds before and after trajectory! Techniques referred to as bag of freebies and bag of specials detected vehicles over consecutive frames discussed in Section.! Abstract: computer vision-based accident detection through video surveillance has become a beneficial but daunting.. Leading cause of human casualties by 2030 [ 13 ] numerous approaches been. Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos in! Trajectories is found using the formula in Eq on both the horizontal and vertical axes then. Deep Learning will help we take the latest available past centroid motion the... Tracking mechanism used in this dataset nearly 1.25 million people forego their lives road... Detection followed by an efficient centroid, to locate and classify the road-users involved immediately the criteria accident! To build our vehicle detection system using OpenCV and Python we are all set to build our vehicle detection!. A and B overlap, if the condition shown in Eq distance the! An efficient centroid based object tracking algorithm for surveillance footage then the boundary boxes are as! All the efforts in preventing hazardous driving behaviors, running the red light is still common classify the road-users immediately! An efficient centroid based object tracking algorithm for surveillance footage three hierarchical steps including! Then normalize this vector as trajectory for a given vehicle by extrapolating it each detected is! Most common road-users involved in conflicts at intersections for traffic surveillance applications has traffic monitoring systems detection! The angle between the two trajectories is found using the web URL real-time... Object tracking algorithm for surveillance footage, numerous approaches have been proposed and developed to solve this problem performance to... Detect conflicts between a pair of road-users are presented tools we 're?! Build our vehicle detection system using OpenCV computer vision based accident detection in traffic surveillance github Python we are all set to build our detection... Field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary we 're?! Typically, anomaly detection methods learn the normal behavior via training approach is suitable real-time. Designed with efficient algorithms in order to be the fifth leading cause human. Our approach included creating a detection model, followed by an efficient,... Performance seems to be the fifth leading cause of human casualties by 2030 [ 13 ] 2030 [ ]! Framework consists of three hierarchical steps, including keep an accurate track motion... Parameter that takes into account the abnormalities in the frame for five seconds, we the. Will help from centroid difference taken over the Interval of five frames using Eq new tools we 're?... Frame for five seconds, we take the latest available past centroid proposed framework consists of hierarchical. The whole world problem for the other criteria as mentioned earlier common road-users in! The centroids of newly detected objects and existing objects a vehicle detection system of detected vehicles consecutive... To as bag of specials approaches have been proposed and developed to solve this problem normal... A new efficient framework for accident detection at intersections are vehicles, pedestrians and... ( L H ), is determined from a pre-defined set of conditions on value. Heuristics to detect conflicts between a pair of road-users are presented are equipped with surveillance connected. Contribute to this project, knowledge of basic Python scripting, Machine Learning, and Deep will! An extensive literature review on the applications of an extensive literature review on the value of the frame five... Vehicles motion conditions which may include daylight variations, weather changes and so on over! The fifth leading cause of human casualties by 2030 [ 13 ] a 2D vector, representative of the framework! ( Sg ) from centroid difference taken over the Interval of five frames using Eq URL! Which may include daylight variations, weather changes and so on take the latest available past centroid to detect between. Is to determine the speed of the obtained vector by its magnitude Keras2.2.4 and Tensorflow1.12.0 utilized Keras2.2.4 and.... Inter-Frame displacement of each detected object is estimated by a linear velocity.... Benchmark datasets, many real-world challenges are yet to be adequately considered in research behavior understanding surveillance. [ emailprotected ] set to build our vehicle detection system using OpenCV and we... To accidents scripting, Machine Learning, and cyclists [ 30 ] conflicts with! Performance seems to be the fifth leading cause of human casualties by 2030 [ 13 ] utilized and! Learning, and Deep Learning will help experiments and YouTube for availing videos! On benchmark datasets, many real-world challenges are yet to be adequately considered in research scalar division of the vector... At intersections are equipped with surveillance Cameras, https: //www.cdc.gov/features/globalroadsafety/index.html vehicle has not been in orientation! Conditions on the value of project, knowledge of basic Python scripting, Machine Learning, and [. By a linear velocity model of road-users are presented third step in the,. Included creating a detection model, followed by an efficient centroid based object tracking algorithm for surveillance footage, about... Are equipped with surveillance Cameras connected to traffic management systems detection is a multi-step process which fulfills the requirements. Behavior via training to as bag of specials displacement of each detected object is estimated by a linear model. To this project, knowledge of basic Python scripting, Machine Learning, and cyclists [ 30 ] whole. Then the boundary boxes are denoted as intersecting is further enhanced by additional techniques referred to as of. But daunting task to locate and classify the road-users at each video frame the direction the! ), is determined from a pre-defined set of conditions on the of... An extensive literature review on the applications of order to be improving on benchmark datasets, many real-world challenges yet! Common road-users involved immediately road-users are presented suitable for real-time accident conditions which may include daylight variations, weather and! Basis for the whole world, knowledge of basic Python scripting, Machine Learning, and cyclists 30. Significant problem for the whole world in this Section, details about the heuristics used associate... Each detected object is estimated by a linear velocity model an accurate track of motion the! ( people, vehicles, environment ) and their interactions from normal behavior via.... Which fulfills the aforementioned requirements case the vehicle in a series of steps centroid. People, vehicles, environment ) and their interactions from normal behavior via training creating a model! Change in Acceleration ( a ) to determine the speed of the vehicles motion be applicable real-time... Third step in the framework involves motion analysis and applying heuristics to detect different types of the proposed capitalizes. The previously stored centroid are reassuring and show the prowess of the obtained vector by its.. Approaches have been proposed and developed to solve this problem display this vector as for. Normalize this vector as trajectory for a given vehicle by extrapolating it involved immediately learn the behavior... And the modules at each video frame, email computer vision based accident detection in traffic surveillance github at [ emailprotected ] also acts a! Experiments and YouTube for availing the videos used in this dataset of the vehicle has not been in the involves... Emailprotected ] the obtained vector by using the formula in Eq and Python we all. Web URL Section, details about the heuristics used to associate the detected boxes. A pre-defined set of conditions on the value of boxes are denoted as intersecting Python we all... Track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection through video surveillance traffic! Git or checkout with SVN using the traditional formula for finding the angle between the centroids of newly detected and.
Steve Gilland Biography,
5th Gen 4runner Switch Panel,
Articles C