关键词: 目标跟踪, 增量学习, 子空间描述, 快速傅立叶变换, 奇异值分解, 粒子滤波

Abstract:

In order to realize stable articulated object tracking,an algorithm based on incremental learning is proposed. In this algorithm,the graph-cut algorithm is used to obtain a foreground image by segmenting the rectangular object region,and a fast Fourier transform is conducted for the foreground image to obtain the Fourier coefficient matrix and to further acquire the amplitude image as the description of the tracking object. Then,the low-dimension subspace representation of the tracking object is obtained by the singular value decomposition and the principle component analysis of the amplitude image. Thus,the tracking algorithm is realized in the framework of particle filtering. Experimental results indicate that the proposed algorithm helps to achieve stable articulated object tracking.

Key words: object tracking, incremental learning, subspace representation, fast Fourier transforms, singular value decomposition, particle filtering