关键词: 目标跟踪, 相关滤波, 边界效应, 最大化间隔

Abstract:

In correlation filter-based tracking methods, redundant dense samples generated by cyclic shifts suffer from both boundary effect and unbalanced categories, which mainly upper bound the training performance. To figure out the two above problems, a margin-maximized correlation filter advanced by mask mechanism is proposed, where the purified samples can be trained in a more effective way. A dual variable is used to convert the original margin maximization problem into a loss minimization one. The final advanced correlation filter can be iteratively solved out by the alternating direction methods of multipliers. Plenty of experimental results indicate that the proposed algorithm can significantly improve the correlation filter. Compared with other tracking algorithms, the proposed tracking algorithm has obvious advantages in speed and location accuracy.

Key words: object tracking, correlation filter, boundary effect, margin maximization