Structured object tracking with discriminative patch attributed relational graph

Abstract

Local features have been widely used in visual tracking to improve robustness in the presence of partial occlusion, deformation, and rotation. In this paper, a structured object tracking algorithm, which uses local discriminative color (LoDC) patch representation and discriminative patch attributed relational graph (DPARG) matching, is proposed. Unlike several existing local feature-based algorithms that divide an object into some rectangular patches of fixed sizes while separately locating each patch to track the object, the proposed algorithm relies on a discriminative color model to distinguish the outstanding colors of the given object. Thus, the multimodal color object is represented by multiple unimodal, homogeneous, and discriminative patches. Moreover, these patches are assembled in a structured DPARG, in which vertexes describe the object’s local discriminative patches while encoding the appearance information, and edges express the relations between vertexes while encoding inner geometric structure information. The object tracking is then formulated as inexact matching of the dynamic undirected graph. The changes of DPARG, along with dynamic environments, are used to filter out invalid patches at the current frame, which usually correspond to those abnormal patches emerging from partial occlusion, similar color disturbances, etc. Finally, the valid patches are assembled to locate the object. The experimental results on the popular tracking benchmark datasets exhibit that the proposed algorithm is reliable enough in tracking even in the presence of serious appearance changes, partial occlusion, and background clutter.