Coupling one-shot learning and online discriminative learning for robust object tracking

Abstract

Visual tracking is a very challenging task in computer vision. In this paper, we present a general-purpose framework for robust tracking. We propose to couple one-shot learning and online discriminative learning together to address the fundamental stability-plasticity issue for tracking. A one-shot learner through offline training on large-scale datasets is used as a stable detector which does not suffer model drift while an online discriminative learner is adopted as the tracker which is adaptive to significant appearance changes. Based on the directive framework, we design a baseline tracking model to verify its effectiveness. In practice, a deep Siamese network trained offline plays as the one-shot learner which can re-detect the target in case of tracking drift and failure. A correlation classifier which incorporates a translation model and a scale model plays as the online learner. Through the coupling of the offline and online learning, the simple baseline tracker achieves a good balance between stability and adaptivity without time-consuming optimization. Experimental results on the large-scale benchmark dataset demonstrate the effectiveness of the proposed framework within which the designed baseline tracker outperforms many state-of-the-art methods both in precision and robustness.