Robust Visual Tracking with Double Bounding Box Model

Abstract

A novel tracking algorithm that can track a highly non-rigid target robustly is proposed using a new bounding box representation called the Double Bounding Box (DBB). In the DBB, a target is described by the combination of the Inner Bounding Box (IBB) and the Outer Bounding Box (OBB). Then our objective of visual tracking is changed to find the IBB and OBB instead of a single bounding box, where the IBB and OBB can be easily obtained by the Dempster-Shafer (DS) theory. If the target is highly non-rigid, any single bounding box cannot include all foreground regions while excluding all background regions. Using the DBB, our method does not directly handle the ambiguous regions, which include both the foreground and background regions. Hence, it can solve the inherent ambiguity of the single bounding box representation and thus can track highly non-rigid targets robustly. Our method finally finds the best state of the target using a new Constrained Markov Chain Monte Carlo (CMCMC)-based sampling method with the constraint that the OBB should include the IBB. Experimental results show that our method tracks non-rigid targets accurately and robustly, and outperforms state-of-the-art methods.