Abstract
This paper presents an enhanced multiple instance learning (MIL) tracker with a suitable representation of object appearance called distribution field descriptor (DF). To address transformations of object template (rotation, scaling), we firstly replace the smoothed histograms used in DF with smoothed bins according the theory of averaged shifted histograms. Secondly, due to the DF specificity and landscape smoothness, we adopt DF descriptor instead of traditional Haar-like one to represent the object appearance. By build object model using selected discriminative layers, our tracker is more robust while needing fewer features than the original tracker. The experimental results show higher performances of our tracker than those of five state-of-the-art ones on several challenging video sequences.
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