Coarse-to-Fine Object Tracking Using Deep Features and Correlation Filters

TL;DR

This paper introduces a novel object tracking method that combines deep features for coarse localization with correlation filters for fine localization, improving robustness and accuracy in tracking tasks.

Contribution

The paper proposes a two-stage tracking algorithm that integrates deep features and correlation filters, with an update mechanism to adapt to appearance changes and prevent model drift.

Findings

Outperforms existing CNN and DCF-based trackers on benchmarks.

Demonstrates robustness to appearance variations and object scale changes.

Achieves high localization precision and tracking stability.

Abstract

During the last years, deep learning trackers achieved stimulating results while bringing interesting ideas to solve the tracking problem. This progress is mainly due to the use of learned deep features obtained by training deep convolutional neural networks (CNNs) on large image databases. But since CNNs were originally developed for image classification, appearance modeling provided by their deep layers might be not enough discriminative for the tracking task. In fact,such features represent high-level information, that is more related to object category than to a specific instance of the object. Motivated by this observation, and by the fact that discriminative correlation filters(DCFs) may provide a complimentary low-level information, we presenta novel tracking algorithm taking advantage of both approaches. We formulate the tracking task as a two-stage procedure. First, we exploit the generalization ability of deep features to coarsely estimate target translation, while ensuring invariance to appearance change. Then, we capitalize on the discriminative power of correlation filters to precisely localize the tracked object. Furthermore, we designed an update control mechanism to learn appearance change while avoiding model drift. We evaluated the proposed tracker on object tracking benchmarks. Experimental results show the robustness of our algorithm, which performs favorably against CNN and DCF-based trackers. Code is available at: https://github.com/AhmedZgaren/Coarse-to-fine-Tracker