TrTr: Visual Tracking with Transformer

TL;DR

This paper introduces TrTr, a visual tracking method that leverages Transformer encoder-decoder architecture to capture global contextual information, outperforming traditional correlation-based trackers on multiple benchmarks.

Contribution

The paper proposes a novel Transformer-based tracker architecture that models global dependencies for improved visual tracking performance.

Findings

Outperforms state-of-the-art on multiple benchmarks

Effective use of Transformer for global context modeling

Competitive accuracy and robustness in tracking tasks

Abstract

Template-based discriminative trackers are currently the dominant tracking methods due to their robustness and accuracy, and the Siamese-network-based methods that depend on cross-correlation operation between features extracted from template and search images show the state-of-the-art tracking performance. However, general cross-correlation operation can only obtain relationship between local patches in two feature maps. In this paper, we propose a novel tracker network based on a powerful attention mechanism called Transformer encoder-decoder architecture to gain global and rich contextual interdependencies. In this new architecture, features of the template image is processed by a self-attention module in the encoder part to learn strong context information, which is then sent to the decoder part to compute cross-attention with the search image features processed by another self-attention module. In addition, we design the classification and regression heads using the output of Transformer to localize target based on shape-agnostic anchor. We extensively evaluate our tracker TrTr, on VOT2018, VOT2019, OTB-100, UAV, NfS, TrackingNet, and LaSOT benchmarks and our method performs favorably against state-of-the-art algorithms. Training code and pretrained models are available at https://github.com/tongtybj/TrTr.