  Robust online multi-person tracking requires the correct associations of
online detection responses with existing trajectories. We address this problem
by developing a novel appearance modeling approach to provide accurate
appearance affinities to guide data association. In contrast to most existing
algorithms that only consider the spatial structure of human appearances, we
exploit the temporal dynamic characteristics within temporal appearance
sequences to discriminate different persons. The temporal dynamic makes a
sufficient complement to the spatial structure of varying appearances in the
feature space, which significantly improves the affinity measurement between
trajectories and detections. We propose a feature selection algorithm to
describe the appearance variations with mid-level semantic features, and
demonstrate its usefulness in terms of temporal dynamic appearance modeling.
Moreover, the appearance model is learned incrementally by alternatively
evaluating newly-observed appearances and adjusting the model parameters to be
suitable for online tracking. Reliable tracking of multiple persons in complex
scenes is achieved by incorporating the learned model into an online
tracking-by-detection framework. Our experiments on the challenging benchmark
MOTChallenge 2015 demonstrate that our method outperforms the state-of-the-art
multi-person tracking algorithms.
