Robust Object Tracking with a Hierarchical Ensemble Framework

TL;DR

This paper introduces a hierarchical ensemble framework for robust object tracking in autonomous robots, combining pixel, patch, and holistic models to improve accuracy under challenging conditions.

Contribution

It presents a novel hierarchical ensemble framework that integrates multiple models for enhanced robustness and accuracy in object tracking tasks.

Findings

Outperforms state-of-the-art algorithms on benchmark sequences.

Handles appearance changes and occlusions effectively.

Demonstrates superior qualitative and quantitative results.

Abstract

Autonomous robots enjoy a wide popularity nowadays and have been applied in many applications, such as home security, entertainment, delivery, navigation and guidance. It is vital to robots to track objects accurately in these applications, so it is necessary to focus on tracking algorithms to improve the robustness and accuracy. In this paper, we propose a robust object tracking algorithm based on a hierarchical ensemble framework which can incorporate information including individual pixel features, local patches and holistic target models. The framework combines multiple ensemble models simultaneously instead of using a single ensemble model individually. A discriminative model which accounts for the matching degree of local patches is adopted via a bottom ensemble layer, and a generative model which exploits holistic templates is used to search for the object through the middle ensemble layer as well as an adaptive Kalman filter. We test the proposed tracker on challenging benchmark image sequences. Both qualitative and quantitative evaluations demonstrate that the proposed tracker performs superiorly against several state-of-the-art algorithms, especially when the appearance changes dramatically and the occlusions occur.