Abstract: The deployment of face tracking capabilities at the network edge requires (near) real-time performance under strict computational and energy constraints. Existing approaches often use object detectors with low complexity for tracking to satisfy limited resource constraints. An obvious problem with limiting complexity is that it has a direct impact on performance (e.g., ability to detect faces), especially at lower resolutions. In this study, we present a novel face tracking system for edge computing devices, which combines a tracking-by-detection algorithm with an ensemble of detectors. This system utilizes an online decision-making strategy based on extracting scene information from a density map to inform the active configuration of object detectors and image resolution. Using this system, we enhance real-time processing capability and reduce energy consumption by adaptively making trade-offs in resolution and active detectors to maintain tracking performance while minimizing resource costs. The proposed face tracking system is coupled with a multi-frame bounding box matching algorithm to provide multi-facial tracking functionality. We demonstrate the effectiveness of our system through experiments using the Multiple Object Tracking (MOT) Head Tracking 21 dataset.
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