Improved fast compressive tracking for low-altitude flying target tracking

Abstract

Effective and efficient low-altitude flying target tracking in the field of visual tracking is challenging due to factors such as background interference, a small target imaging area, scale changes, and in-plane/out-of-plane rotation. Fast compressive tracking is an effective algorithm that combines compressive sensing theory and the naive Bayes classifier to track targets in real-time. Since the target motion information is not used in the tracking process and a fixed learning rate is adopted, the target may be lost during tracking, especially when the background interference is considerable or when in-plane/out-of-plane rotation exists. To solve this problem, first, target motion information was introduced to reduce the search area for predicting the target position. Then, the confidence calculation was optimized by comprehensively considering the posterior probability of the candidate region and the positive sample membership value. Finally, the learning rate was dynamically adjusted according to the target velocity and optimized confidence. Experimental results verified that the proposed method could effectively improve the efficiency, accuracy, and robustness of target tracking.