OMKT: Projection based bounded on-line multiple kernel tracker

Abstract

This paper addresses the problem of on-line learning for object tracking. Although a variety of techniques have been proposed in literature, a recent benchmark reveals that none of them can work well in all scenarios due to numerous practical challenges, such as illumination variations, motion blur, etc. These challenges occur at different time frames making it hard to design a tracker. In this paper, a machine learning framework for object tracking is investigated, which can integrate with a variety of feature and kernel engineering techniques in dealing with many challenges in different scenarios. By following the successful tracking-by-detection methodology, this paper proposes OMKT — On-line Multiple Kernel Tracking scheme, which attempts to tackle the object tracking task by exploring recent advances of on-line multiple kernel learning techniques in machine learning. In particular, OMKT sequentially learns the best tracker for each individual kernel via on-line Projectron++ learning, and at the same time attempts to identify the optimal combination of multiple kernel trackers using the Hedge algorithm. In contrast to many existing schemes in literature, OMKT learns both the kernel classifiers and their combination on-line, hence it can adapt to tracking changes faster. Furthermore, the projection strategy in Projectron++ alleviates the difficulty of pre-specifying a budget size for support-set. Promising experimental results on a recent benchmark reveal usefulness of OMKT.