Robust visual tracking via randomly projected instance learning

Purpose: The purpose of this paper is to design a robust tracking algorithm which is suitable for the real-time requirement and solves the mistake labeling issue in the appearance model of trackers with the spare features. Design/methodology/approach: This paper proposes a tracker to select the most discriminative randomly projected ferns and integrates a coarse-to-fine search strategy in this framework. First, the authors exploit multiple instance boosting learning to maximize the bag likelihood and select randomly projected fern from feature pool to degrade the effect of mistake labeling. Second, a coarse-to-fine search approach is first integrated into the framework of multiple instance learning (MIL) for less detections. Findings: The quantitative and qualitative experiments demonstrate that the tracker has shown favorable performance in efficiency and effective among the competitors of tracking algorithms. Originality/value: The proposed method selects the feature from the compressive domain by MIL AnyBoost and integrates the coarse-to-fine search strategy first to reduce the burden of detection. This paper designs a tracker with high speed and favorable results which is more suitable for real-time scene. © 2017, © Emerald Publishing Limited.