Modeling and Propagating CNNs in a Tree Structure for Visual Tracking

TL;DR

This paper introduces an online visual tracking method that uses a tree-structured ensemble of CNNs to handle multi-modality in target appearances, improving robustness and efficiency in tracking tasks.

Contribution

It proposes a novel tree-structured CNN ensemble for online tracking, enabling multi-modality handling and reliable model updates with shared parameters to reduce computational costs.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Effectively manages multi-modality in target appearances

Maintains high tracking accuracy with low computational overhead

Abstract

We present an online visual tracking algorithm by managing multiple target appearance models in a tree structure. The proposed algorithm employs Convolutional Neural Networks (CNNs) to represent target appearances, where multiple CNNs collaborate to estimate target states and determine the desirable paths for online model updates in the tree. By maintaining multiple CNNs in diverse branches of tree structure, it is convenient to deal with multi-modality in target appearances and preserve model reliability through smooth updates along tree paths. Since multiple CNNs share all parameters in convolutional layers, it takes advantage of multiple models with little extra cost by saving memory space and avoiding redundant network evaluations. The final target state is estimated by sampling target candidates around the state in the previous frame and identifying the best sample in terms of a weighted average score from a set of active CNNs. Our algorithm illustrates outstanding performance compared to the state-of-the-art techniques in challenging datasets such as online tracking benchmark and visual object tracking challenge.