Evaluating Lookup-Based Monocular Human Pose Tracking on the HumanEva Test Data
neural information processing systems(2006)
摘要
This work presents an evaluation of several lookup-based methods for recovering three-dimensional human pose from monocular video sequences. The methods themselves are largely described elsewhere (1, 2), although the work presented here incorporates a few minor enhancements. The primary contribution of this work is the evaluation of the results on a data set with ground truth available, which allows for quantitative comparisons with other techniques. Methods relying upon silhouettes produced via background subtraction tend to act as a "straw man" in relation to the current state of the art; many recently proposed techniques work without reliance upon background subtraction and cite this feature as one of their advantages. Without disputing such reasonable claims, this work seeks to push the envelope for background-subtraction methods as far as possible. The goal of this effort is to provide a challenging baseline against which the performance of various alternatives may be assessed.
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