Abstract
In traffic scenarios, vehicle trajectories can provide almost all the dynamic information of moving vehicles. Analyzing the vehicle trajectory in the monitoring scene can grasp the dynamic road traffic information. Cross-camera association of vehicle trajectories in multiple cameras can break the isolation of target information between single cameras and obtain the overall road operation conditions in a large-scale video surveillance area, which helps road traffic managers to conduct traffic analysis, prediction, and control. Based on the framework of DBT automatic target detection, this paper proposes a cross-camera vehicle trajectory correlation matching method based on the Euclidean distance metric correlation of trajectory points. For the multitarget vehicle trajectory acquired in a single camera, we first perform 3D trajectory reconstruction based on the combined camera calibration in the overlapping area and then complete the similarity association between the cross-camera trajectories and the cross-camera trajectory update, and complete the trajectory transfer of the vehicle between adjacent cameras. Experiments show that the method in this paper can well solve the problem that the current tracking technology is difficult to match the vehicle trajectory under different cameras in complex traffic scenes and essentially achieves long-term and long-distance continuous tracking and trajectory acquisition of multiple targets across cameras.
Highlights
Target tracking is one of the research hot spots in computer vision, and it has been widely used in military, unmanned driving, video monitoring, and other fields. e current target tracking algorithm [1] can be divided into three categories from the observation model: the method based on the generated model, the method based on the discriminant model, and the method based on deep learning
E method based on the generative model is called the classical target tracking algorithm. is method extracts the features of the target in the current frame, constructs the target model, and searches the best matching region with the appearance model in the frame as the prediction position of the target
Based on the minimum output sum of squared error (MOSSE) algorithm [3], correlation filtering is applied to target tracking for the first time
Summary
Target tracking is one of the research hot spots in computer vision, and it has been widely used in military, unmanned driving, video monitoring, and other fields. e current target tracking algorithm [1] can be divided into three categories from the observation model: the method based on the generated model, the method based on the discriminant model, and the method based on deep learning. E method based on the discriminant model regards the target tracking problem as a classification or regression problem. In this method, the target is separated from the background by combining the background information with the feature extraction. In view of the target deformation, scale change, and occlusion in the process of long-time target tracking, TLD combines tracking with a traditional detection algorithm and updates the model and parameters online to make the tracking more robust and reliable. Due to the complexity of the model, the tracking speed is slow, which cannot meet the practical application It is still an important research task to track multitarget continuously and track accurately in complex traffic scenes. It is of great value to improve the utilization efficiency of traffic video monitoring data, timely and accurately to grasp road traffic information and regional road operation status. e cross-camera multitarget tracking can solve the problem that monocular camera cannot track accurately for a long time and a long distance, which lays an important foundation for the acquisition of wide-angle traffic information
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