SMTrack: State-Aware Mamba for Efficient Temporal Modeling in Visual Tracking
Yinchao Ma, Dengqing Yang, Zhangyu He, Wenfei Yang, Tianzhu Zhang

TL;DR
SMTrack introduces a novel state-aware space model for efficient long-range temporal modeling in visual tracking, achieving robust performance with low computational costs without complex modules.
Contribution
The paper proposes SMTrack, a new paradigm that models long-range temporal dependencies efficiently using a state-aware space model, avoiding complex modules and high computational costs.
Findings
Achieves promising tracking performance.
Maintains low computational costs.
Facilitates long-range temporal interactions.
Abstract
Visual tracking aims to automatically estimate the state of a target object in a video sequence, which is challenging especially in dynamic scenarios. Thus, numerous methods are proposed to introduce temporal cues to enhance tracking robustness. However, conventional CNN and Transformer architectures exhibit inherent limitations in modeling long-range temporal dependencies in visual tracking, often necessitating either complex customized modules or substantial computational costs to integrate temporal cues. Inspired by the success of the state space model, we propose a novel temporal modeling paradigm for visual tracking, termed State-aware Mamba Tracker (SMTrack), providing a neat pipeline for training and tracking without needing customized modules or substantial computational costs to build long-range temporal dependencies. It enjoys several merits. First, we propose a novel…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Gaze Tracking and Assistive Technology · Human Pose and Action Recognition
