Visual Target Tracking using Robust Information Interaction between Single Tracker and Online Model

Abstract

In this paper, a novel tracking algorithm based on the cooperative operation of online appearance model and typical tracking in contiguous frames is proposed. First of all, to achieve satisfactory performances in challenging scenes, we focus on establishing a robust discriminative tracking model with linear Support Vector Machine (SVM) and use the particle filter for localization. Intended to fit the particle filter, the outputs of SVM classifier are mapped into probabilities with a sigmoid function so that the posterior of candidate samples is estimated. Then, the tracking loop starts with median flow method and the coordinated operation of the two trackers is mediated by the maximum a posteriori (MAP) estimate for the target probability of negative samples, which is defined during the sigmoid fit. Lastly, for the purpose of model update, we sum up the optimal SVM using a prototype set with the predefined budget, and the classifier is updated on both the prototype set and the updated data from the tracking results every few frames. A number of comparative experiments are conducted on real video sequences and both qualitative and quantitative evaluations demonstrate a robust and precise performance of our method.