关键词: 自适应, 学习速率, 高斯核尺度因子, 颜色特征（CN）跟踪器

Abstract: With a brief description of target tracker based on Spatial-Temporal Context（STC） and Circulant Structure with Kernels（CSK）, it mainly introduces the tracker based on Color Name（CN）, and in the light of tracking drift resulting from the occlusion of targets, scale variation or illumination changes, presents an improved method based on adaptive learning rate and adaptive Gauss-kernel scale factor, aiming at improving the training model updating and marking to reduce the cumulative error of the target model and increase the accuracy of the tracking process. In the experiment, it chooses ten types of videos and uses three evaluation parameters to compare the tracking performances between initial and improved algorithms. The results show that the improved algorithm has better robustness for occlusion, illumination changes and scale variation, which is applied to the pedestrian tracking of Unmanned Aerial Vehicle（UAV）, achieving good performance.

Key words: adaptive, learning-rate, Gauss-kernel scale factor, Color Name（CN）-tracker