Correlation filter-based self-paced object tracking

Abstract

Object tracking is an important capability for robots tasked with interacting with humans and the environment, and it enables robots to manipulate objects. In object tracking, selecting samples to learn a robust and efficient appearance model is a challenging task. Model learning determines both the strategy and frequency of model updating, which concerns many details that can affect the tracking results. In this paper, we propose an object tracking approach by formulating a new objective function that integrates the learning paradigm of self-paced learning into object tracking such that reliable samples can be automatically selected for model learning. Sample weights and model parameters can be learned by minimizing this single objective function under the framework of kernelized correlation filters. Moreover, a real-valued error-tolerant self-paced function with a constraint vector is proposed to combine prior knowledge, i.e., the characteristics of object tracking, with information learned during tracking. We demonstrate the robustness and efficiency of our object tracking approach on a recent object tracking benchmark data set: OTB 2013.