关键词: 目标跟踪, 多任务, 增量子空间学习, 实时字典扩充

Abstract: In object tracking, target state is an uncertain nonlinear task and it needs to process some large-scale dynamic data. The object representation needs to learn more precise models from the complicated data in order to adopt the variations caused by intrinsic or extrinsic factors during tracking. This paper proposes a novel object template learning scheme and efficient multi-task structured sparse representation method. In this framework, firstly it learns some low dimensional subspace basis vectors（positive template） from tracked data using Incremental Principal Component Analysis and augments them with some instantaneous negative samples generated from every frame. Then the observation model can be represented linearly by using object templates with some additive i.i.d.（independently and identically distributed） Gaussian-
Laplacian noise assumption. Finally, the tracker estimates the most accurate object state among particles based on the highest likelihood between candidate samples and the real object information. Meanwhile an efficient template update rule is used for adopting the variations of object such that caused by illumination, occlusion, pose, background cluttering and motion blur etc. Extensive experiments have been carried out to validate the new algorithm that each state covers a specific property of the object in every task, then the tracker is designed by associating them and well represents the observation model. Experimental results show that the proposed algorithm performs favorably against several state-of-the-art trackers.

Key words: object tracking, multi-task, incremental subspace learning, template expansion