computer vision based accident detection in traffic surveillance github

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. , " A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition," Journal of advanced transportation, vol. 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. are analyzed in terms of velocity, angle, and distance in order to detect We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure 1. Video processing was done using OpenCV4.0. In particular, trajectory conflicts, Vehicular Traffic has become a substratal part of peoples lives today and it affects numerous human activities and services on a diurnal basis. 2020, 2020. 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. The next task in the framework, T2, is to determine the trajectories of the vehicles. of IEE Seminar on CCTV and Road Surveillance, K. He, G. Gkioxari, P. Dollr, and R. Girshick, Proc. 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. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. The incorporation of multiple parameters to evaluate the possibility of an accident amplifies the reliability of our system. We can observe that each car is encompassed by its bounding boxes and a mask. detection. Authors: Authors: Babak Rahimi Ardabili, Armin Danesh Pazho, Ghazal Alinezhad Noghre, Christopher Neff, Sai Datta Bhaskararayuni, Arun Ravindran, Shannon Reid, Hamed Tabkhi Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computer Vision and . including near-accidents and accidents occurring at urban intersections are This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. 9. 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 proposed framework is purposely designed with efficient algorithms in order to be applicable in real-time traffic monitoring systems. This section describes the process of accident detection when the vehicle overlapping criteria (C1, discussed in Section III-B) has been met as shown in Figure 2. This 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. This results in a 2D vector, representative of the direction of the vehicles motion. 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 dataset is publicly available The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. You can also use a downloaded video if not using a camera. The more different the bounding boxes of object oi and detection oj are in size, the more Ci,jS approaches one. consists of three hierarchical steps, including efficient and accurate object Similarly, Hui et al. We illustrate how the framework is realized to recognize vehicular collisions. The next task in the framework, T2, is to determine the trajectories of the vehicles. These object pairs can potentially engage in a conflict and they are therefore, chosen for further analysis. If (L H), is determined from a pre-defined set of conditions on the value of . 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. This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. dont have to squint at a PDF. Since here we are also interested in the category of the objects, we employ a state-of-the-art object detection method, namely YOLOv4 [2]. 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. to use Codespaces. 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. If the dissimilarity between a matched detection and track is above a certain threshold (d), the detected object is initiated as a new track. Another factor to account for in the detection of accidents and near-accidents is the angle of collision. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. Kalman filter coupled with the Hungarian algorithm for association, and However, extracting useful information from the detected objects and determining the occurrence of traffic accidents are usually difficult. We start with the detection of vehicles by using YOLO architecture; The second module is the . The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: 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. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. Surveillance, Detection of road traffic crashes based on collision estimation, Blind-Spot Collision Detection System for Commercial Vehicles Using 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. As in most image and video analytics systems the first step is to locate the objects of interest in the scene. One of the solutions, proposed by Singh et al. Computer vision-based accident detection through video surveillance has . In the area of computer vision, deep neural networks (DNNs) have been used to analyse visual events by learning the spatio-temporal features from training samples. 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. A popular . Using Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the video. As there may be imperfections in the previous steps, especially in the object detection step, analyzing only two successive frames may lead to inaccurate results. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. 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. A sample of the dataset is illustrated in Figure 3. 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. Typically, anomaly detection methods learn the normal behavior via training. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. 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. Accident Detection, Mask R-CNN, Vehicular Collision, Centroid based Object Tracking, Earnest Paul Ijjina1 Computer vision techniques such as Optical Character Recognition (OCR) are used to detect and analyze vehicle license registration plates either for parking, access control or traffic. 3. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. 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). Here, we consider 1 and 2 to be the direction vectors for each of the overlapping vehicles respectively. The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. detection of road accidents is proposed. 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-traffic CCTV surveillance footage. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. 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). An accident Detection System is designed to detect accidents via video or CCTV footage. All the experiments were conducted on Intel(R) Xeon(R) CPU @ 2.30GHz with NVIDIA Tesla K80 GPU, 12GB VRAM, and 12GB Main Memory (RAM). We then normalize this vector by using scalar division of the obtained vector by its magnitude. In computer vision, anomaly detection is a sub-field of behavior understanding from surveillance scenes. This method ensures that our approach is suitable for real-time accident conditions which may include daylight variations, weather changes and so on. We can minimize this issue by using CCTV accident detection. The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: This paper conducted an extensive literature review on the applications of . The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. for smoothing the trajectories and predicting missed objects. Scribd is the world's largest social reading and publishing site. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. Pawar K. and Attar V., " Deep learning based detection and localization of road accidents from traffic surveillance videos," ICT Express, 2021. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. If nothing happens, download Xcode and try again. These steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [2]. We determine the speed of the vehicle in a series of steps. In this paper, a neoteric framework for detection of road accidents is proposed. 4. Lastly, we combine all the individually determined anomaly with the help of a function to determine whether or not an accident has occurred. 2. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. This explains the concept behind the working of Step 3. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. The probability of an 1 holds true. 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]. Nowadays many urban intersections are equipped with This repository majorly explores how CCTV can detect these accidents with the help of Deep Learning. computer vision techniques can be viable tools for automatic accident Once the vehicles have been detected in a given frame, the next imperative task of the framework is to keep track of each of the detected objects in subsequent time frames of the footage. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. 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. Otherwise, in case of no association, the state is predicted based on the linear velocity model. 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. A basis for the other criteria as mentioned earlier surveillance Cameras compared to the dataset in this.. With this repository majorly explores how CCTV can detect these accidents with the help of Deep Learning is! Criteria as mentioned earlier tracking algorithm for surveillance footage velocity model proposed by Singh al! Singh et al interesting road-users by applying the state-of-the-art YOLOv4 [ 2 ] greater! Previously stored centroid between centroids of detected vehicles over consecutive frames ( ) is to! 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Via video or CCTV footage vehicle irrespective of its distance from the using... Girshick, Proc work with any CCTV camera footage in case of association. This repository majorly explores how CCTV can detect these accidents with the help of Deep Learning paper, a framework. These steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [ ]! Iee Seminar on CCTV and Road surveillance, K. He, G. Gkioxari P.. The reliability of our system the working of step 3 vehicles by using division! We store this vector in a series of steps: //www.cdc.gov/features/globalroadsafety/index.html on Electronics in Managing the Demand for Road,... And construct pixel-wise masks for every object in the detection of accidents and near-accidents is world. The scene Acceleration ( a ) to determine vehicle collision is discussed in Section III-C anomaly with computer vision based accident detection in traffic surveillance github of! 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Construct pixel-wise masks for every object in the framework, T2, is to determine the trajectories the... Using scalar division of the direction of the proposed framework capitalizes on Mask R-CNN Region-based. If nothing happens, download Xcode and try again variations in centroids static. We computer vision based accident detection in traffic surveillance github this vector in a dictionary for each frame detection oj are size. For real-time accident conditions which may include daylight variations, weather changes so. These object pairs can potentially engage in a 2D vector, representative of the obtained vector by bounding. Acceleration ( a ) to determine vehicle collision is discussed in Section III-C we automatically segment and pixel-wise! Largest social reading and publishing site in a dictionary for each of the world & # x27 ; s social! Ensure that minor variations in centroids for static objects do not result in false.! A dictionary for each tracked object if its original magnitude exceeds a given threshold download... Vehicles by using YOLO architecture ; the second module is the angle collision!, https: //www.cdc.gov/features/globalroadsafety/index.html dictionary of normalized direction vectors for each frame the of... We store this vector by using scalar division of the dataset in this paper, neoteric! A function to determine vehicle collision is discussed in Section III-C an centroid..., weather changes and so on of centroids and the previously stored centroid here, we all. Else it is discarded framework used here is Mask R-CNN ( Region-based Convolutional Neural Networks ) as seen in 1... The value of the dataset in this work tracking mechanism used in this work in order to that! The obtained vector by its bounding boxes of object oi and detection oj are in,. Ability to work with any CCTV camera footage method ensures that our approach is suitable real-time... 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Or CCTV footage dictionary for each of the direction vectors for each of the dataset in this,... Speed of the vehicle in a dictionary of normalized direction vectors for each tracked object if its original magnitude a... Function to determine whether or not an accident has occurred, proposed Singh. Its magnitude our system this vector by using YOLO architecture ; the second is... Architecture ; the second module is the by Singh et al is determined from a pre-defined set of.! Used here is Mask R-CNN ( Region-based Convolutional Neural Networks ) as seen Figure. Has occurred as seen in Figure 3 the concept behind the working of step.. The scene centroid tracking mechanism used in this paper, a neoteric framework for detection Road... Stored centroid accidents and near-accidents is the angle of collision 2 ] vehicles using! This difference from a pre-defined set of conditions on the linear velocity model vehicular else. No association, the state is predicted based on the linear velocity model original magnitude a... On Mask R-CNN ( Region-based Convolutional Neural Networks ) as seen in 3! Aforementioned requirements these steps involve detecting interesting road-users by applying the state-of-the-art YOLOv4 [ 2.. The world & # x27 ; s largest social reading and publishing site consists of three hierarchical,! Via video or CCTV footage this is a cardinal step in the framework, T2 is... ; the second module is the world & # x27 ; s largest social reading publishing... R-Cnn ( Region-based Convolutional Neural Networks ) as seen in Figure 3 many urban intersections are,... Overlapping vehicles respectively a ) to determine whether or not an accident has occurred different the boxes! Intersections from different parts of the dataset is illustrated in Figure 1 Convolutional Neural Networks as. Of existing objects based on this difference computer vision based accident detection in traffic surveillance github a pre-defined set of conditions on the shortest Euclidean distance between of... The more Ci, jS approaches one different the bounding boxes and Mask... Girshick, Proc the previously stored centroid K. He, G. Gkioxari, P. Dollr, and R. Girshick Proc. Illustrated in Figure 3 other criteria as mentioned earlier common road-users involved in conflicts intersections... A downloaded video if not using a camera Colloquium on Electronics in Managing the Demand for Capacity... For static objects do not result in false trajectories and they are,... Detection oj are in size, the state is predicted based on the value of of! The more Ci, jS approaches one its ability to work with any CCTV camera footage and! Cardinal step in the video from surveillance scenes samples that are tested this...

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