Real-Time Tracking with Online Constrained Compressive Learning

概要

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In object tracking, a recent trend is using "Tracking by Detection" technique which trains a discriminative online classifier to detect objects from background. However, the incorrect updating of the online classifier and insufficient features used during the online learning often lead to the drift problems. In this work we propose an online random fern classifier with a simple but effective compressive feature in a framework integrating the online classifier, the optical-flow tracker and an update model. The compressive feature is a random projection from highly dimensional multi-scale image feature space to a low-dimensional representation by a sparse measurement matrix, which is expect to contain more information. An update model is proposed to detect tracker failure, correct tracker result and constrain the updating of online classifier, thus reducing the chance of wrong updating in online training. Our method runs at real-time and the experimental results show performance improvement compared to other state-of-the-art approaches on several challenging video clips.