动态调整事件流建模，提升多模态目标跟踪精度与鲁棒性。
Event-Adaptive State Transition and Gated Fusion for RGB-Event Object Tracking
- 根据事件密度动态调节状态转移矩阵，区分处理稀疏与密集事件流。
- 在FE108和FELT数据集上达到当前最优性能，推理轻量适合嵌入式部署。
- 通过自适应门控融合机制，有效抑制噪声并保留跨模态互补信息。
基于Vision Mamba的RGB-Event（RGBE）跟踪方法普遍使用静态状态转移矩阵，无法适应事件稀疏性的变化，导致对稀疏事件流建模不足、对密集事件流过拟合，从而降低跨模态融合的鲁棒性。为此，我们提出MambaTrack，一种基于动态状态空间模型（DSSM）的多模态高效跟踪框架。首先，引入事件自适应状态转移机制，依据事件流密度动态调制状态转移矩阵，通过可学习标量控制状态演化速率，实现对稀疏与密集事件流的差异化建模。其次，设计门控投影融合（GPF）模块，将RGB特征投影至事件特征空间，并基于事件密度与RGB置信度生成自适应门控，精确调控融合强度，抑制噪声同时保留互补信息。实验表明，MambaTrack在FE108与FELT数据集上均达到当前最优性能，其轻量化设计具备实时嵌入式部署潜力。
原文摘要 · Abstract (English)
Existing Vision Mamba-based RGB-Event(RGBE) tracking methods suffer from using static state transition matrices, which fail to adapt to variations in event sparsity. This rigidity leads to imbalanced modeling-underfitting sparse event streams and overfitting dense ones-thus degrading cross-modal fusion robustness. To address these limitations, we propose MambaTrack, a multimodal and efficient tracking framework built upon a Dynamic State Space Model(DSSM). Our contributions are twofold. First, we introduce an event-adaptive state transition mechanism that dynamically modulates the state transition matrix based on event stream density. A learnable scalar governs the state evolution rate, enabling differentiated modeling of sparse and dense event flows. Second, we develop a Gated Projection Fusion(GPF) module for robust cross-modal integration. This module projects RGB features into the event feature space and generates adaptive gates from event density and RGB confidence scores. These gates precisely control the fusion intensity, suppressing noise while preserving complementary information. Experiments show that MambaTrack achieves state-of-the-art performance on the FE108 and FELT datasets. Its lightweight design suggests potential for real-time embedded deployment.
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