Robust Object Tracking with a Hierarchical Ensemble Framework
Mengmeng Wang, Yong Liu

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…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · IoT-based Smart Home Systems · Face recognition and analysis
