Adaptive Siamese Tracking with a Compact Latent Network

TL;DR

This paper introduces an adaptive Siamese tracking method using a compact latent network that leverages decisive initial frame samples for quick scene adaptation, improving accuracy and speed across multiple trackers.

Contribution

It proposes a novel compact latent network and training strategy that enhances Siamese trackers' adaptability and discrimination with minimal computational overhead.

Findings

Achieves superior accuracy on six datasets

Maintains high running speed during tracking

Effectively adapts to scene variations

Abstract

In this paper, we provide an intuitive viewing to simplify the Siamese-based trackers by converting the tracking task to a classification. Under this viewing, we perform an in-depth analysis for them through visual simulations and real tracking examples, and find that the failure cases in some challenging situations can be regarded as the issue of missing decisive samples in offline training. Since the samples in the initial (first) frame contain rich sequence-specific information, we can regard them as the decisive samples to represent the whole sequence. To quickly adapt the base model to new scenes, a compact latent network is presented via fully using these decisive samples. Specifically, we present a statistics-based compact latent feature for fast adjustment by efficiently extracting the sequence-specific information. Furthermore, a new diverse sample mining strategy is designed for training to further improve the discrimination ability of the proposed compact latent network. Finally, a conditional updating strategy is proposed to efficiently update the basic models to handle scene variation during the tracking phase. To evaluate the generalization ability and effectiveness and of our method, we apply it to adjust three classical Siamese-based trackers, namely SiamRPN++, SiamFC, and SiamBAN. Extensive experimental results on six recent datasets demonstrate that all three adjusted trackers obtain the superior performance in terms of the accuracy, while having high running speed.