Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking

TL;DR

This paper introduces Siamese Cascaded RPN (C-RPN), a multi-stage framework for real-time visual tracking that improves discrimination and localization accuracy by leveraging multi-level features and sequential refinement.

Contribution

The paper proposes a novel multi-stage Siamese RPN with feature transfer blocks, hard negative sampling, and progressive refinement, enhancing tracking accuracy and robustness over previous methods.

Findings

Achieves state-of-the-art results on multiple benchmarks.

Runs in real-time with high accuracy.

Effectively handles distractors and scale variations.

Abstract

Region proposal networks (RPN) have been recently combined with the Siamese network for tracking, and shown excellent accuracy with high efficiency. Nevertheless, previously proposed one-stage Siamese-RPN trackers degenerate in presence of similar distractors and large scale variation. Addressing these issues, we propose a multi-stage tracking framework, Siamese Cascaded RPN (C-RPN), which consists of a sequence of RPNs cascaded from deep high-level to shallow low-level layers in a Siamese network. Compared to previous solutions, C-RPN has several advantages: (1) Each RPN is trained using the outputs of RPN in the previous stage. Such process stimulates hard negative sampling, resulting in more balanced training samples. Consequently, the RPNs are sequentially more discriminative in distinguishing difficult background (i.e., similar distractors). (2) Multi-level features are fully leveraged through a novel feature transfer block (FTB) for each RPN, further improving the discriminability of C-RPN using both high-level semantic and low-level spatial information. (3) With multiple steps of regressions, C-RPN progressively refines the location and shape of the target in each RPN with adjusted anchor boxes in the previous stage, which makes localization more accurate. C-RPN is trained end-to-end with the multi-task loss function. In inference, C-RPN is deployed as it is, without any temporal adaption, for real-time tracking. In extensive experiments on OTB-2013, OTB-2015, VOT-2016, VOT-2017, LaSOT and TrackingNet, C-RPN consistently achieves state-of-the-art results and runs in real-time.