Metric Learning Driven Multi-Task Structured Output Optimization for Robust Keypoint Tracking

TL;DR

This paper introduces a robust keypoint tracker that jointly optimizes temporal coherence, spatial consistency, and discriminative features using a multi-task structured output approach driven by metric learning, improving tracking robustness.

Contribution

It presents a novel joint learning framework that simultaneously models temporal, spatial, and feature discriminability aspects for keypoint tracking, which was not effectively addressed before.

Findings

Demonstrated improved tracking accuracy on benchmark datasets.

Effectively balances temporal coherence, spatial consistency, and feature discriminability.

Outperforms existing keypoint tracking methods in robustness and precision.

Abstract

As an important and challenging problem in computer vision and graphics, keypoint-based object tracking is typically formulated in a spatio-temporal statistical learning framework. However, most existing keypoint trackers are incapable of effectively modeling and balancing the following three aspects in a simultaneous manner: temporal model coherence across frames, spatial model consistency within frames, and discriminative feature construction. To address this issue, we propose a robust keypoint tracker based on spatio-temporal multi-task structured output optimization driven by discriminative metric learning. Consequently, temporal model coherence is characterized by multi-task structured keypoint model learning over several adjacent frames, while spatial model consistency is modeled by solving a geometric verification based structured learning problem. Discriminative feature construction is enabled by metric learning to ensure the intra-class compactness and inter-class separability. Finally, the above three modules are simultaneously optimized in a joint learning scheme. Experimental results have demonstrated the effectiveness of our tracker.