Object tracking via online trajectory optimization with multi-feature fusion
Weijian Ruan, Jun Chen, Chao Liang, Yi Wu, Ruimin Hu · 2017
The goal of object tracking is to estimate the state and trajectory of an interested target in a video sequence, thus both spatial and temporal information are of critical importance for tracking. However, most existing trackers usually determine targets just by the judgement like confidence from a single frame, which tend to treat tracking as a static detecting problem while neglecting the spatial-temporal relationship. In this paper, we propose a novel tracking method of online trajectory optimization with multi-feature fusion (TOFF). Considering the trajectory continuity, the accurate targets are determined by estimating the optimal short trajectories over all the video fragments, which can be obtained by the observation models that are iteratively updated based on the selected most reliable proposals. By employing the structured samples instead of binary-labeled samples, we construct a structured output model with representing descriptor of multi-feature fusion as the basic tracker. Extensive experiments on various challenging image sequences demonstrate the superiority of our method to several state-of-the-art methods.