Abstract
Single-object tracking (SOT) on edge devices is a critical computer vision task, requiring accurate and continuous target localization across video frames under occlusion, distractor interference, and fast motion. However, recent state-of-the-art distractor-aware memory mechanisms are largely built on segmentation-based trackers and rely on mask prediction and attention-driven memory updates, which introduce substantial computational overhead and limit real-time deployment on resource-constrained hardware; meanwhile, lightweight trackers sustain high throughput but are prone to drift when visually similar distractors appear. To address these challenges, we propose EdgeDAM, a lightweight detection-guided tracking framework that reformulates distractor-aware memory for bounding-box tracking under strict edge constraints. EdgeDAM introduces two key strategies: (1) Dual-Buffer Distractor-Aware Memory (DAM), which integrates a Recent-Aware Memory to preserve temporally consistent target hypotheses and a Distractor-Resolving Memory to explicitly store hard negative candidates and penalize their re-selection during recovery; and (2) Confidence-Driven Switching with Held-Box Stabilization, where tracker reliability and temporal consistency criteria adaptively activate detection and memory-guided re-identification during occlusion, while a held-box mechanism temporarily freezes and expands the estimate to suppress distractor contamination. Extensive experiments on five benchmarks, including the distractor-focused DiDi dataset, demonstrate improved robustness under occlusion and fast motion while maintaining real-time performance on mobile devices, achieving 88.2% accuracy on DiDi and 25 FPS on an iPhone 15. Code will be released.
Abstract (translated)
边缘设备上的单目标跟踪（SOT）是一项关键的计算机视觉任务，要求在遮挡、干扰物和快速运动的情况下，在视频帧中进行准确且连续的目标定位。然而，最近最先进的干扰感知内存机制大多基于分割追踪器，并依赖于掩码预测和注意力驱动的记忆更新，这带来了大量的计算开销，限制了其在资源受限硬件上的实时部署；同时，轻量级的跟踪器虽然能够保持高吞吐量，但在视觉相似的干扰物出现时容易发生漂移。为了解决这些挑战，我们提出了EdgeDAM，这是一种轻量级的检测引导追踪框架，它将干扰感知内存重新构架以适应边界框追踪，并在严格的边缘约束条件下进行设计。EdgeDAM引入了两种关键策略：（1）双缓冲器干扰感知内存(Dual-Buffer Distractor-Aware Memory, DAM)，该策略集成了一个最近感知记忆模块来保留时间上一致的目标假设，并且使用一个干扰物解析内存模块明确存储难例负样本并在恢复期间惩罚其重新选择；（2）信心驱动切换和保框稳定化机制，这种机制根据追踪器可靠性和时间一致性标准自适应地激活检测和记忆引导的重识别，在遮挡时同时引入了一个保框机制暂时冻结并扩展估计以抑制干扰物污染。在包括专注于干扰物的DiDi数据集在内的五个基准测试上进行了广泛的实验，结果显示EdgeDAM在保持移动设备实时性能的同时，提高了遮挡和快速运动下的鲁棒性，并且在DiDi数据集中达到了88.2%的准确率，在iPhone 15上的帧率为25FPS。代码将在稍后发布。
URL
https://arxiv.org/abs/2603.05463
