Graph Embedding Based Semi-Supervised Discriminative Tracker

Abstract

Recently, constructing a good graph to represent data structures is widely used in machine learning based applications. Some existing trackers have adopted graph construction based classifiers for tracking. However, their graph structures are not effective to characterize the inter-class separability and multi-model sample distribution, both of which are very important to successful tracking. In this paper, we propose to use a new graph structure to improve tracking performance without the assistance of learning object subspace generatively as previous work did. Meanwhile, considering the test samples deviate from the distribution of the training samples in tracking applications, we formulate the discriminative learning process, to avoid over fitting, in a semi-supervised fashion as L1-graph based regularizer. In addition, a non-linear variant is extended to adapt to multi-modal sample distribution. Experimental results demonstrate the superior properties of the proposed tracker.