# Unsupervised learning-based long-term superpixel tracking

## TL;DR

This paper introduces an unsupervised learning approach for long-term superpixel tracking in videos, combining context-rich features and multi-step integration to improve correspondence accuracy over extended sequences.

## Contribution

It proposes a novel two-step pipeline that uses unsupervised learning for superpixel matching and multi-step path integration for enhanced long-term tracking accuracy.

## Key findings

- Outperforms state-of-the-art methods in superpixel matching accuracy
- Multi-step integration improves long-term correspondence reliability
- Effective in extended video sequences for object tracking

## Abstract

Finding correspondences between structural entities decomposing images is of high interest for computer vision applications. In particular, we analyze how to accurately track superpixels - visual primitives generated by aggregating adjacent pixels sharing similar characteristics - over extended time periods relying on unsupervised learning and temporal integration. A two-step video processing pipeline dedicated to long-term superpixel tracking is proposed. First, unsupervised learning-based superpixel matching provides correspondences between consecutive and distant frames using new context-rich features extended from greyscale to multi-channel and forward-backward consistency contraints. Resulting elementary matches are then combined along multi-step paths running through the whole sequence with various inter-frame distances. This produces a large set of candidate long-term superpixel pairings upon which majority voting is performed. Video object tracking experiments demonstrate the accuracy of our elementary estimator against state-of-the-art methods and proves the ability of multi-step integration to provide accurate long-term superpixel matches compared to usual direct and sequential integration.