动态注意力机制提升追踪记忆网络在复杂场景下的精度与效率
Dynamic Attention Mechanism in Spatiotemporal Memory Networks for Object Tracking
- 通过分析时空关联性自适应调整注意力权重
- 在OTB-2015等数据集上实现最高成功率与鲁棒性
- 适合需要实时高精度追踪的复杂环境应用
主流视觉目标追踪框架多依赖模板匹配，其性能严重依赖模板特征质量，在目标形变、遮挡和背景干扰等复杂场景下难以维持。现有基于时空记忆的追踪器虽强调记忆容量扩展，但缺乏有效的动态特征选择与自适应融合机制。为此，我们提出时空记忆网络中的动态注意力机制（DASTM），包含两项关键创新：1）可微分的动态注意力机制，通过分析模板与记忆特征间的时空相关性，自适应调整通道-空间注意力权重；2）轻量级门控网络，根据目标运动状态自主分配计算资源，优先保留挑战场景下的高区分度特征。在OTB-2015、VOT 2018、LaSOT和GOT-10K等多个基准上的大量实验表明，DASTM在成功率、鲁棒性和实时效率方面均达到当前最优水平，为复杂环境下实时追踪提供了新方案。
原文摘要 · Abstract (English)
Mainstream visual object tracking frameworks predominantly rely on template matching paradigms. Their performance heavily depends on the quality of template features, which becomes increasingly challenging to maintain in complex scenarios involving target deformation, occlusion, and background clutter. While existing spatiotemporal memory-based trackers emphasize memory capacity expansion, they lack effective mechanisms for dynamic feature selection and adaptive fusion. To address this gap, we propose a Dynamic Attention Mechanism in Spatiotemporal Memory Network (DASTM) with two key innovations: 1) A differentiable dynamic attention mechanism that adaptively adjusts channel-spatial attention weights by analyzing spatiotemporal correlations between the templates and memory features; 2) A lightweight gating network that autonomously allocates computational resources based on target motion states, prioritizing high-discriminability features in challenging scenarios. Extensive evaluations on OTB-2015, VOT 2018, LaSOT, and GOT-10K benchmarks demonstrate our DASTM's superiority, achieving state-of-the-art performance in success rate, robustness, and real-time efficiency, thereby offering a novel solution for real-time tracking in complex environments.
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