Efficient Hand Articulations Tracking using Adaptive Hand Model and Depth map
Byeongkeun Kang, Yeejin Lee, and Truong Q. Nguyen

TL;DR
This paper presents a real-time, GPU-free hand tracking system that adapts to different hand shapes and uses depth maps for accurate articulation tracking, suitable for mobile and wearable devices.
Contribution
It introduces an adaptive hand model, improved initialization, and hierarchical pixel sampling to enable efficient, personalized hand tracking without high-performance GPUs.
Findings
Achieves real-time hand tracking without GPUs
Handles different hand shapes without personalization
Improves accuracy with hierarchical pixel sampling
Abstract
Real-time hand articulations tracking is important for many applications such as interacting with virtual / augmented reality devices or tablets. However, most of existing algorithms highly rely on expensive and high power-consuming GPUs to achieve real-time processing. Consequently, these systems are inappropriate for mobile and wearable devices. In this paper, we propose an efficient hand tracking system which does not require high performance GPUs. In our system, we track hand articulations by minimizing discrepancy between depth map from sensor and computer-generated hand model. We also initialize hand pose at each frame using finger detection and classification. Our contributions are: (a) propose adaptive hand model to consider different hand shapes of users without generating personalized hand model; (b) improve the highly efficient frame initialization for robust tracking and…
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Taxonomy
TopicsHand Gesture Recognition Systems · Human Pose and Action Recognition · Gaze Tracking and Assistive Technology
