AbstractMost existing tracking algorithms construct a representation of a target object prior to the tracking task starts, and utilize invariant features tohandle appearance variation of the target caused by lighting, pose, and view angle change. In this paper, we present an efficient and effec-tive online algorithm that incrementally learns and adapts a low dimensional eigenspace representation to reflect appearance changes of the tar-get, thereby facilitating the tracking task. Furthermore, our incremental method correctly updates the sample mean and the eigenbasis, whereasexisting incremental subspace update methods ignore the fact the sample mean varies over time. The tracking problem is formulated as a stateinference problem within a Markov Chain Monte Carlo framework and a particle filter is incorporated for propagating sample distributions overtime. Numerous experiments demonstrate the effectiveness of the proposed tracking algorithm in indoor and outdoor environments where thetarget objects undergo large pose and lighting changes. 1 Introduction The main challenges of visual tracking can be attributed to the difficulty in handling appear-ance variability of a target object. Intrinsic appearance variabilities include pose variatio
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.