关键词: 视觉目标跟踪, TLD（Tracking-Learning-Detector）算法, 核相关滤波, 目标检测自矫正, 支持向量机（SVM）

Abstract:

With the wide application of Unmanned Aerial Vehicle（UAV） in aerial fields such as aerial photography, surveying and mapping, environmental monitoring and express delivery, the requirements for the availability and reliability of micro-miniature drones are higher, so the function of precise positioning is essential. Con-ducting fast and robust tracking of the target is a key issue for precision positioning of the UAV. The TLD（Tracking Learning Detector） algorithm has provided an effective solution for it. Although many scholars have studied it and improved the traditional TLD algorithm, the tracking accuracy and speed of the algorithms are still difficult to meet the landing requirements of UAV. Under the framework of TLD, this paper proposes an algorithm for automatically determining the target through target’s shape features, which improves the autonomy of the positioning process. Kernelized Correlation Filter（KCF） is used as tracker in the tracking module of TLD framework. The tracker improves the real-time, accuracy and robustness of the algorithm; at the same time, a Histogram of Gradient（HOG） and Support Vector Machine（SVM） is used in the positioning process to achieve self-correction of target detection to ensure accurate target recognition. 7 types of test video simulating landing of UAV are tested to verify the performance of the proposed algorithm. Compared with the other three tracking algorithms and tested in the landing experiment, the test results show that the robustness and accuracy of the algorithm are better than other algorithms and the processing speed can reach 31.47 f/s; Therefore, the algorithm proposed uses the kernel correlation filter as the tracker in the TLD framework, combines the tracking and detection results effectively, approves the processing speed and adds self-correction of target detection to ensure the tracking accuracy, then the full autonomous precision positioning of the UAV can effectively achieve.

Key words: visual tracking, TLD（Tracking-Learning-Detection） algorithm, kernel correlation, self-correction of target detection, Support Vector Machine（SVM）