Online Metric-Weighted Linear Representations for Robust Visual Tracking

TL;DR

This paper introduces a robust visual tracking method using online metric-weighted linear representations that adapt to appearance changes and incorporate object identification, improving accuracy in challenging sequences.

Contribution

The paper presents a novel online metric learning approach integrated into linear appearance modeling, enhancing robustness and enabling automatic object identification during tracking.

Findings

Significantly improves tracker robustness on sequences with drastic appearance changes.

Effectively combines tracking and recognition for object identification.

Demonstrates superior performance on challenging video sequences.

Abstract

In this paper, we propose a visual tracker based on a metric-weighted linear representation of appearance. In order to capture the interdependence of different feature dimensions, we develop two online distance metric learning methods using proximity comparison information and structured output learning. The learned metric is then incorporated into a linear representation of appearance. We show that online distance metric learning significantly improves the robustness of the tracker, especially on those sequences exhibiting drastic appearance changes. In order to bound growth in the number of training samples, we design a time-weighted reservoir sampling method. Moreover, we enable our tracker to automatically perform object identification during the process of object tracking, by introducing a collection of static template samples belonging to several object classes of interest. Object identification results for an entire video sequence are achieved by systematically combining the tracking information and visual recognition at each frame. Experimental results on challenging video sequences demonstrate the effectiveness of the method for both inter-frame tracking and object identification.