Abstract
Abstract. It is the focus of current research that how to realize high precision and real-time dynamic monitoring and tracking of moving targets by video satellites because of instantaneous and dynamic continuous observation of targets in a certain area by the video satellites. The existing detection and tracking methods for moving objects have target misdetection and missed detection, which reduces the accuracy of moving object detection. In this paper, a Tracking Correction Detection Correction (TCD) method is proposed to solve these problems. Firstly, the background model is established by using the improved ViBe target detection algorithm, and the moving target mask is obtained by adaptive threshold calculation. By using pyramid structure iterative algorithm, the moving object can be classified as noise or real object according to the set of detection results of different detection windows. The high-order correlation vector tracking method is used to modify the detection result of the moving target acquired in the previous frame, and finally the accurate detection result of the moving target is obtained. The comparison analysis between the frame difference (FD) method, GMM method, ViBe method and TCD method shows that the TCD method has better robustness for noise, light and background dynamic changes, and the test results of TCD method are more complete and the real-time is better. It is proved by this work that the accuracy of the target detection of TCD method has reached 85%, which has a high engineering application value.
Highlights
Video satellite is one of the hot spots in the development of remote sensing satellite
A Track Before Detect (TBD) method has been proposed for moving object detection, which mainly includes dynamic programming track before detect (DP-TBD) (Meng N et al, 2019), particle filter track before detect (PF-TBD) and Hough Transform track before detect (HT-TBD)
The results show that the precision, recall and F-Score of the Tracking Correction Detection Correction (TCD) algorithm are all outperform the other three methods
Summary
Video satellite is one of the hot spots in the development of remote sensing satellite. The ViBe Algorithm has a good robustness and high detection precision, but there may be a lot of holes in the moving object, a fault in the middle of the moving object and the ghost phenomenon in the process of building the background sample when dealing with complex scene (Zhang Y et al, 2017). MCMC Data Association method (He, Z et al, 2019), Network Flow method (Azadi Moghaddam Arani, A et al, 2019), K shortest path method (KSP) and minimum Clique graph optimization method (Yue H et al, 2019, Porretta, L et al, 2019), etc These methods only consider the second-order relationship between targets, they are not robust when dealing with nonlinear motion in dense scenes or frequent occlusion. Through the method of tracking postdetection, the target after ViBe detection is further corrected to improve the detection accuracy of the moving target
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