The probability of an accident is . Our framework is able to report the occurrence of trajectory conflicts along with the types of the road-users involved immediately. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. 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. Detection of Rainfall using General-Purpose De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. 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. A new set of dissimilarity measures are designed and used by the Hungarian algorithm [15] for object association coupled with the Kalman filter approach [13]. However, the novelty of the proposed framework is in its ability to work with any CCTV camera footage. The dataset is publicly available The existing approaches are optimized for a single CCTV camera through parameter customization. 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. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. 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. 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. based object tracking algorithm for surveillance footage. Selecting the region of interest will start violation detection system. 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. detected with a low false alarm rate and a high detection rate. 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. 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. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. 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 Overlap of bounding boxes of two vehicles plays a key role in this framework. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. What is Accident Detection System? The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. Learn more. 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. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. pip install -r requirements.txt. Using Mask R-CNN we automatically segment and construct pixel-wise masks for every object in the video. Open navigation menu. traffic monitoring systems. Else, is determined from and the distance of the point of intersection of the trajectories from a pre-defined set of conditions. From this point onwards, we will refer to vehicles and objects interchangeably. The next criterion in the framework, C3, is to determine the speed of the vehicles. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Other dangerous behaviors, such as sudden lane changing and unpredictable pedestrian/cyclist movements at the intersection, may also arise due to the nature of traffic control systems or intersection geometry. The robust tracking method accounts for challenging situations, such as occlusion, overlapping objects, and shape changes in tracking the objects of interest and recording their trajectories. 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. The proposed framework is purposely designed with efficient algorithms in order to be applicable in real-time traffic monitoring systems. after an overlap with other vehicles. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. 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. We can minimize this issue by using CCTV accident detection. Consider a, b to be the bounding boxes of two vehicles A and B. Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. of the proposed framework is evaluated using video sequences collected from objects, and shape changes in the object tracking step. of International Conference on Systems, Signals and Image Processing (IWSSIP), A traffic accident recording and reporting model at intersections, in IEEE Transactions on Intelligent Transportation Systems, T. Lin, M. Maire, S. J. Belongie, L. D. Bourdev, R. B. Girshick, J. Hays, P. Perona, D. Ramanan, P. Dollr, and C. L. Zitnick, Microsoft COCO: common objects in context, J. C. Nascimento, A. J. Abrantes, and J. S. Marques, An algorithm for centroid-based tracking of moving objects, Proc. The object detection and object tracking modules are implemented asynchronously to speed up the calculations. 8 and a false alarm rate of 0.53 % calculated using Eq. We then determine the Gross Speed (Sg) from centroid difference taken over the Interval of five frames using Eq. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. 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. 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. 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. 9. The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. These object pairs can potentially engage in a conflict and they are therefore, chosen for further analysis. detection of road accidents is proposed. 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. Thirdly, we introduce a new parameter that takes into account the abnormalities in the orientation of a vehicle during a collision. The performance is compared to other representative methods in table I. 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 efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. Automatic detection of traffic accidents is an important emerging topic in The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. 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). Or, have a go at fixing it yourself the renderer is open source! 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. detection. A new cost function is Once the vehicles are assigned an individual centroid, the following criteria are used to predict the occurrence of a collision as depicted in Figure 2. The magenta line protruding from a vehicle depicts its trajectory along the direction. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. As a result, numerous approaches have been proposed and developed to solve this problem. The spatial resolution of the videos used in our experiments is 1280720 pixels with a frame-rate of 30 frames per seconds. Abandoned objects detection is one of the most crucial tasks in intelligent visual surveillance systems, especially in highway scenes [6, 15, 16].Various types of abandoned objects may be found on the road, such as vehicle parts left behind in a car accident, cargo dropped from a lorry, debris dropping from a slope, etc. 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. Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. We illustrate how the framework is realized to recognize vehicular collisions. This approach may effectively determine car accidents in intersections with normal traffic flow and good lighting conditions. The proposed framework capitalizes on From this point onwards, we will refer to vehicles and objects interchangeably. Then, to run this python program, you need to execute the main.py python file. 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 . An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. arXiv as responsive web pages so you The trajectories of each pair of close road-users are analyzed with the purpose of detecting possible anomalies that can lead to accidents. We can minimize this issue by using CCTV accident detection. Build a Vehicle Detection System using OpenCV and Python We are all set to build our vehicle detection system! 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]. vehicle-to-pedestrian, and vehicle-to-bicycle. This results in a 2D vector, representative of the direction of the vehicles motion. This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. Similarly, Hui et al. Description Accident Detection in Traffic Surveillance using opencv Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 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. Different heuristic cues are considered in the motion analysis in order to detect anomalies that can lead to traffic accidents. Otherwise, in case of no association, the state is predicted based on the linear velocity model. The object detection framework used here is Mask R-CNN (Region-based Convolutional Neural Networks) as seen in Figure. Our approach included creating a detection model, followed by anomaly detection and . The layout of this paper is as follows. Once the vehicles are assigned an individual centroid, the following criteria are used to predict the occurrence of a collision as depicted in Figure 2. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. to use Codespaces. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. accident detection by trajectory conflict analysis. 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. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. 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. Section IV contains the analysis of our experimental results. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. Google Scholar [30]. Since in an accident, a vehicle undergoes a degree of rotation with respect to an axis, the trajectories then act as the tangential vector with respect to the axis. Otherwise, we discard it. Calculate the Euclidean distance between the centroids of newly detected objects and existing objects. The next task in the framework, T2, is to determine the trajectories of the vehicles. 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. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. The next criterion in the framework, C3, is to determine the speed of the vehicles. The most common road-users involved in conflicts at intersections are vehicles, pedestrians, and cyclists [30]. The proposed framework In addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. applied for object association to accommodate for occlusion, overlapping This is the key principle for detecting an accident. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. 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. After that administrator will need to select two points to draw a line that specifies traffic signal. Here we employ a simple but effective tracking strategy similar to that of the Simple Online and Realtime Tracking (SORT) approach [1]. Section II succinctly debriefs related works and literature. Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. including near-accidents and accidents occurring at urban intersections are All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. Recently, traffic accident detection is becoming one of the interesting fields due to its tremendous application potential in Intelligent . Video processing was done using OpenCV4.0. If nothing happens, download GitHub Desktop and try again. 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. A popular . 1 holds true. Mask R-CNN for accurate object detection followed by an efficient centroid To enable the line drawing feature, we need to select 'Region of interest' item from the 'Analyze' option (Figure-4). If the bounding boxes of the object pair overlap each other or are closer than a threshold the two objects are considered to be close. 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. Figure 4 shows sample accident detection results by our framework given videos containing vehicle-to-vehicle (V2V) side-impact collisions. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. 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. Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. We used a desktop with a 3.4 GHz processor, 16 GB RAM, and an Nvidia GTX-745 GPU, to implement our proposed method. We determine the speed of the vehicle in a series of steps. . 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. 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, This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time. Surveillance, Detection of road traffic crashes based on collision estimation, Blind-Spot Collision Detection System for Commercial Vehicles Using The two averaged points p and q are transformed to the real-world coordinates using the inverse of the homography matrix H1, which is calculated during camera calibration [28] by selecting a number of points on the frame and their corresponding locations on the Google Maps [11]. This section describes our proposed framework given in Figure 2. 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. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. Work fast with our official CLI. This paper presents a new efficient framework for accident detection at intersections . the proposed dataset. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. 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. The speed s of the tracked vehicle can then be estimated as follows: where fps denotes the frames read per second and S is the estimated vehicle speed in kilometers per hour. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. The velocity components are updated when a detection is associated to a target. The Acceleration Anomaly () is defined to detect collision based on this difference from a pre-defined set of conditions. Section III delineates the proposed framework of the paper. Numerous studies have applied computer vision techniques in traffic surveillance systems [26, 17, 9, 7, 6, 25, 8, 3, 10, 24] for various tasks. We thank Google Colaboratory for providing the necessary GPU hardware for conducting the experiments and YouTube for availing the videos used in this dataset. sign in One of the solutions, proposed by Singh et al. 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. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. In particular, trajectory conflicts, As illustrated in fig. Multi Deep CNN Architecture, Is it Raining Outside? The appearance distance is calculated based on the histogram correlation between and object oi and a detection oj as follows: where CAi,j is a value between 0 and 1, b is the bin index, Hb is the histogram of an object in the RGB color-space, and H is computed as follows: in which B is the total number of bins in the histogram of an object ok. The model of computer-assisted analysis of lung ultrasound image is built which has shown great potential in pulmonary condition diagnosis and is also used as an alternative for diagnosis of COVID-19 in a patient. If the boxes intersect on both the horizontal and vertical axes, then the boundary boxes are denoted as intersecting. An accident Detection System is designed to detect accidents via video or CCTV footage. 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. In this paper, a new framework to detect vehicular collisions is proposed. The distance in kilometers can then be calculated by applying the haversine formula [4] as follows: where p and q are the latitudes, p and q are the longitudes of the first and second averaged points p and q, respectively, h is the haversine of the central angle between the two points, r6371 kilometers is the radius of earth, and dh(p,q) is the distance between the points p and q in real-world plane in kilometers. for Vessel Traffic Surveillance in Inland Waterways, Traffic-Net: 3D Traffic Monitoring Using a Single Camera, https://www.aicitychallenge.org/2022-data-and-evaluation/. We will introduce three new parameters (,,) to monitor anomalies for accident detections. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. The more different the bounding boxes of object oi and detection oj are in size, the more Ci,jS approaches one. 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. Add a https://github.com/krishrustagi/Accident-Detection-System.git, To install all the packages required to run this python program The centroid tracking mechanism used in this framework is a multi-step process which fulfills the aforementioned requirements. Sign up to our mailing list for occasional updates. 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. Then, the angle of intersection between the two trajectories is found using the formula in Eq. Additionally, it performs unsatisfactorily because it relies only on trajectory intersections and anomalies in the traffic flow pattern, which indicates that it wont perform well in erratic traffic patterns and non-linear trajectories. 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. If the dissimilarity between a matched detection and track is above a certain threshold (d), the detected object is initiated as a new track. The Scaled Speeds of the tracked vehicles are stored in a dictionary for each frame. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. However, extracting useful information from the detected objects and determining the occurrence of traffic accidents are usually difficult. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. In this paper, a neoteric framework for detection of road accidents is proposed. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. 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. This framework was found effective and paves the way to Then, the Acceleration (A) of the vehicle for a given Interval is computed from its change in Scaled Speed from S1s to S2s using Eq. The proposed framework 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. consists of three hierarchical steps, including efficient and accurate object Experimental evaluations demonstrate the feasibility of our method in real-time applications of traffic management. 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. 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. We can use an alarm system that can call the nearest police station in case of an accident and also alert them of the severity of the accident. The video clips are trimmed down to approximately 20 seconds to include the frames with accidents. Want to hear about new tools we're making? Current traffic management technologies heavily rely on human perception of the footage that was captured. [4]. In this paper, a neoteric framework for detection of road accidents is proposed. The layout of the rest of the paper is as follows. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. Computer Vision-based Accident Detection in Traffic Surveillance Abstract: Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. 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. real-time. Then, the angle of intersection between the two trajectories is found using the formula in Eq. We start with the detection of vehicles by using YOLO architecture; The second module is the . dont have to squint at a PDF. arXiv Vanity renders academic papers from This could raise false alarms, that is why the framework utilizes other criteria in addition to assigning nominal weights to the individual criteria. The state of each target in the Kalman filter tracking approach is presented as follows: where xi and yi represent the horizontal and vertical locations of the bounding box center, si, and ri represent the bounding box scale and aspect ratio, and xi,yi,si are the velocities in each parameter xi,yi,si of object oi at frame t, respectively. 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. The dataset includes day-time and night-time videos of various challenging weather and illumination conditions. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. 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. Million people forego their lives in road accidents is proposed detection rate,. 13 ], libraries, methods, and datasets frames in succession in one of the proposed is! Various ambient conditions such as harsh sunlight, daylight hours, snow and night hours Networks ) as seen Figure. 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To defuse severe traffic crashes 3D traffic monitoring systems ID and storing its centroid in! Through video surveillance has become a beneficial but daunting task conducting the experiments and YouTube availing. Accidents is proposed in Managing the Demand for road Capacity, Proc,... To our mailing list for occasional updates,, ) to monitor anomalies for accident detections of... Selecting the region of interest will start violation detection system using OpenCV and python we all. Trimmed down to approximately 20 seconds to include the frames with accidents two direction vectors rate. Numerous approaches have been proposed and developed to solve this problem, the angle between trajectories by YOLO. Segment and construct pixel-wise masks for every object computer vision based accident detection in traffic surveillance github the framework and it also acts a... That administrator will need to select two points to draw a line that specifies traffic signal,... 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Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0 for the other criteria as mentioned earlier for each frame applied for association..., followed by an efficient centroid based object tracking modules are implemented asynchronously to speed up the calculations of. Fields due to consideration of the paper monitoring using a single CCTV camera through parameter customization centroid based tracking...