Traffic closed-circuit television (CCTV) devices can be used to detect and track objects on roads by designing and applying artificial intelligence and deep learning models. If nothing happens, download GitHub Desktop and try again. 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 first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. The use of change in Acceleration (A) to determine vehicle collision is discussed in Section III-C. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. The layout of the rest of the paper is as follows. Keyword: detection Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety. The proposed framework provides a robust This paper conducted an extensive literature review on the applications of . This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. Section III delineates the proposed framework of the paper. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. Then, we determine the angle between trajectories by using the traditional formula for finding the angle between the two direction vectors. An automatic accident detection framework provides useful information for adjusting intersection signal operation and modifying intersection geometry in order to defuse severe traffic crashes. 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. of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. 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 addition, large obstacles obstructing the field of view of the cameras may affect the tracking of vehicles and in turn the collision detection. 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. This paper introduces a solution which uses state-of-the-art supervised deep learning framework. for smoothing the trajectories and predicting missed objects. Abstract: In Intelligent Transportation System, real-time systems that monitor and analyze road users become increasingly critical as we march toward the smart city era. Work fast with our official CLI. including near-accidents and accidents occurring at urban intersections are A sample of the dataset is illustrated in Figure 3. The parameters are: When two vehicles are overlapping, we find the acceleration of the vehicles from their speeds captured in the dictionary. We used a desktop with a 3.4 GHz processor, 16 GB RAM, and an Nvidia GTX-745 GPU, to implement our proposed method. of bounding boxes and their corresponding confidence scores are generated for each cell. Then the approaching angle of the a pair of road-users a and b is calculated as follows: where denotes the estimated approaching angle, ma and mb are the the general moving slopes of the road-users a and b with respect to the origin of the video frame, xta, yta, xtb, ytb represent the center coordinates of the road-users a and b at the current frame, xta and yta are the center coordinates of object a when first observed, xtb and ytb are the center coordinates of object b when first observed, respectively. Currently, I am experimenting with cutting-edge technology to unleash cleaner energy sources to power the world.<br>I have a total of 8 . 7. In this paper, a neoteric framework for detection of road accidents is proposed. The efficacy of the proposed approach is due to consideration of the diverse factors that could result in a collision. Support vector machine (SVM) [57, 58] and decision tree have been used for traffic accident detection. Annually, human casualties and damage of property is skyrocketing in proportion to the number of vehicular collisions and production of vehicles [14]. A sample of the dataset is illustrated in Figure 3. 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. We then normalize this vector by using scalar division of the obtained vector by its magnitude. If (L H), is determined from a pre-defined set of conditions on the value of . Many people lose their lives in road accidents. 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, have demonstrated an approach that has been divided into two parts. 3. In this paper, a new framework to detect vehicular collisions is proposed. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. Next, we normalize the speed of the vehicle irrespective of its distance from the camera using Eq. traffic monitoring systems. 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. at: http://github.com/hadi-ghnd/AccidentDetection. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. De-register objects which havent been visible in the current field of view for a predefined number of frames in succession. 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. 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. Each video clip includes a few seconds before and after a trajectory conflict. This is done in order to ensure that minor variations in centroids for static objects do not result in false trajectories. 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. The proposed framework capitalizes on Consider a, b to be the bounding boxes of two vehicles A and B. The Trajectory Anomaly () is determined from the angle of intersection of the trajectories of vehicles () upon meeting the overlapping condition C1. We illustrate how the framework is realized to recognize vehicular collisions. Note: This project requires a camera. In this section, details about the heuristics used to detect conflicts between a pair of road-users are presented. The existing video-based accident detection approaches use limited number of surveillance cameras compared to the dataset in this work. Computer Vision-based Accident Detection in Traffic Surveillance Abstract: Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. Use Git or checkout with SVN using the web URL. If the dissimilarity between a matched detection and track is above a certain threshold (d), the detected object is initiated as a new track. 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. 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). accident is determined based on speed and trajectory anomalies in a vehicle 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. 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. The dataset includes accidents in various ambient conditions such as harsh sunlight, daylight hours, snow and night hours. 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. This section provides details about the three major steps in the proposed accident detection framework. 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. 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. Update coordinates of existing objects based on the shortest Euclidean distance from the current set of centroids and the previously stored centroid. From this point onwards, we will refer to vehicles and objects interchangeably. 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. different types of trajectory conflicts including vehicle-to-vehicle, 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. The process used to determine, where the bounding boxes of two vehicles overlap goes as follow: Import Libraries Import Video Frames And Data Exploration The second part applies feature extraction to determine the tracked vehicles acceleration, position, area, and direction. The surveillance videos at 30 frames per second (FPS) are considered. 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 . 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). The object detection and object tracking modules are implemented asynchronously to speed up the calculations. 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. This algorithm relies on taking the Euclidean distance between centroids of detected vehicles over consecutive frames. We store this vector in a dictionary of normalized direction vectors for each tracked object if its original magnitude exceeds a given threshold. We then normalize this vector by using scalar division of the obtained vector by its magnitude. This framework is based on local features such as trajectory intersection, velocity calculation and their anomalies. Road traffic crashes ranked as the 9th leading cause of human loss and account for 2.2 per cent of all casualties worldwide [13]. However, extracting useful information from the detected objects and determining the occurrence of traffic accidents are usually difficult. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. They are also predicted to be the fifth leading cause of human casualties by 2030 [13]. Numerous studies have applied computer vision techniques in traffic surveillance systems [26, 17, 9, 7, 6, 25, 8, 3, 10, 24] for various tasks. 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