Online Tracking Based on Multiple Appearances Model
Tracking target in a long-term is still a big challenge in computer vision. In recent research, many researchers pay much attention on updating current appearance of tracking target to build one online appearance model. However, one appearance model is always not enough to describe historical appearance information especially for long-term tracking task. In this paper, we propose an online multiple appearances model based on Dirichlet Process Mixture Model (DPMM), which can make different appearance representations of the tracking target grouped dynamically and in an unsupervised way. Since DPMM's appealing properties are characterized by Gibbs sampling and Gibbs sampling costs too much, we proposed an online Bayesian learning algorithm instead of Gibbs sampling to reliably and efficiently learn a DPMM from scratch through sequential approximation in a streaming fashion to adapt new tracking targets. Experiments on multiple challenging benchmark public dataset demonstrate the proposed tracking algorithm performs favorably against the state-of-the-art.