Dynamic Attention Mechanism in Spatiotemporal Memory Networks for Object Tracking
This work addresses the challenge of robust object tracking for computer vision applications, offering a novel solution that is incremental but effective in complex environments.
The paper tackled the problem of maintaining tracking performance in complex scenarios like target deformation and occlusion by proposing a dynamic attention mechanism in spatiotemporal memory networks, resulting in state-of-the-art performance on benchmarks such as OTB-2015 and VOT 2018 with improved success rate and real-time efficiency.
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.