EdgeDAM: Real-time Object Tracking for Mobile Devices

• URL: http://arxiv.org/abs/2603.05463v1
• Date: Thu, 05 Mar 2026 18:35:25 GMT
• Title: EdgeDAM: Real-time Object Tracking for Mobile Devices
• Abstract summary: Single-object tracking on edge devices is a critical computer vision task.<n>Recent state-of-the-art distractor-aware memory mechanisms are largely built on segmentation-based trackers.<n>We propose EdgeDAM, a detection-guided tracking framework that reformulates distractor-aware memory for bounding-box tracking.
• Score: 6.26511531314926
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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