Video Tracking Using Learned Hierarchical Features

TL;DR

This paper introduces a hierarchical feature learning approach for visual object tracking, combining offline learned features with online domain adaptation to handle complex motions and appearance changes.

Contribution

It presents a novel two-layer CNN with temporal slowness constraints and an online adaptation module for improved tracking performance.

Findings

Significant performance improvements on videos with complex motions.

Robustness to appearance changes of target objects.

Effective integration with existing tracking methods.

Abstract

In this paper, we propose an approach to learn hierarchical features for visual object tracking. First, we offline learn features robust to diverse motion patterns from auxiliary video sequences. The hierarchical features are learned via a two-layer convolutional neural network. Embedding the temporal slowness constraint in the stacked architecture makes the learned features robust to complicated motion transformations, which is important for visual object tracking. Then, given a target video sequence, we propose a domain adaptation module to online adapt the pre-learned features according to the specific target object. The adaptation is conducted in both layers of the deep feature learning module so as to include appearance information of the specific target object. As a result, the learned hierarchical features can be robust to both complicated motion transformations and appearance changes of target objects. We integrate our feature learning algorithm into three tracking methods. Experimental results demonstrate that significant improvement can be achieved using our learned hierarchical features, especially on video sequences with complicated motion transformations.