Long-term correlation tracking

TLDR

Long‑term visual tracking must handle significant appearance changes due to deformation, abrupt motion, heavy occlusion, and out‑of‑view. The study aims to solve this long‑term tracking problem by developing a robust algorithm. The method decomposes tracking into translation and scale estimation and employs an online random fern classifier to re‑detect objects after failure. Correlation of temporal context improves translation accuracy and reliability, discriminative correlation filters learned from confident frames enhance scale estimation, and experiments on large‑scale benchmarks show the algorithm outperforms state‑of‑the‑art methods in efficiency, accuracy, and robustness.

Abstract

In this paper, we address the problem of long-term visual tracking where the target objects undergo significant appearance variation due to deformation, abrupt motion, heavy occlusion and out-of-view. In this setting, we decompose the task of tracking into translation and scale estimation of objects. We show that the correlation between temporal context considerably improves the accuracy and reliability for translation estimation, and it is effective to learn discriminative correlation filters from the most confident frames to estimate the scale change. In addition, we train an online random fern classifier to re-detect objects in case of tracking failure. Extensive experimental results on large-scale benchmark datasets show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy, and robustness.