Realtime and Robust Hand Tracking from Depth
CVPR(2014)
摘要
We present a realtime hand tracking system using a depth sensor. It tracks a fully articulated hand under large viewpoints in realtime (25 FPS on a desktop without using a GPU) and with high accuracy (error below 10 mm). To our knowledge, it is the first system that achieves such robustness, accuracy, and speed simultaneously, as verified on challenging real data. Our system is made of several novel techniques. We model a hand simply using a number of spheres and define a fast cost function. Those are critical for realtime performance. We propose a hybrid method that combines gradient based and stochastic optimization methods to achieve fast convergence and good accuracy. We present new finger detection and hand initialization methods that greatly enhance the robustness of tracking.
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关键词
robustness,fingerprint identification,stochastic optimization method,realtime hand tracking,depth sensor,icp,finger detection,realtime performance,hand initialization method,convergence,object tracking,gradient methods,robust hand tracking,hand tracking,hand tracking, icp, pso,pso,gradient based optimization method,cost function,real-time systems,stochastic programming,accuracy,tracking,real time systems
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