MaST: Motion-aware Sparse Pipeline for Lightweight Object Tracking
Transformer-based object trackers are renowned for their strong performance, yet dense token processing often leads to prohibitive computational cost, limiting real-time deployment on edge devices. While recent works explore token pruning to reduce computation, they often stop short of an end-to-end sparse pipeline, as early-layer token scores can be noisy without a motion prior, and many trackers ultimately fall back to dense reshaping to feed the dense prediction head that partially negates the savings. We introduce Motion-aware Sparse Tracker (MaST), a sparse tracking framework that makes sparsity effective from tokens to boxes. First, MaST injects a lightweight motion prior to refine ...
| Authors | Qingmao Wei, Fagui Liu, Dengke Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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