Mask-Guided Siamese Tracking With a Frequency-Spatial Hybrid Network

Abstract

Current tracking methods often adopt a compact template to emphasize target-specific features, alongside an expansive search region to encapsulate surrounding environmental information. However, the employment of a small template size may result in the loss of critical contextual information, which can be particularly harmful in challenging scenarios. Moreover, current tracking methods predominantly focus on spatial or channel operations, neglecting the potential of the frequency domain. To resolve those issues, we propose a novel Mask-Guided Siamese Tracking (MGTrack) framework to enhance tracking efficacy from two perspectives. Firstly, we propose an innovative Template Mask Encoder (TME) that employs a large template to produce a learnable mask embedding, thus preserving more surrounding contextual cues while focusing on target-oriented discriminative features. Secondly, we propose a frequency-spatial hybrid network, which is composed of a Frequency-Spatial Fusion (FSF) module and a Frequency-Spatial Attention (FSA) module. Particularly, the FSF module integrates frequency blocks with local and global fusion blocks, effectively aggregating deep semantic features from the backbone network with shallow texture features. Additionally, the FSA module enables bidirectional information exchange between spatial and frequency attention during the feature interaction process. Experiments across short-term and long-term tracking benchmarks demonstrate that our MGTrack can achieve better tracking performance with fewer parameters and FLOPs than some state-of-the-art tracking frameworks. The code of our MGTrack is available at https://github.com/jiabingxiing/MGTrack.