A Bayesian Framework for Sparse Representation-Based 3D Human Pose Estimation
This work addresses the problem of unreliable pose estimation in computer vision when training data is limited, offering a robust solution for applications like motion analysis or robotics.
The paper tackles 3D human pose estimation from monocular images by introducing a Bayesian framework based on sparse representation, which learns dictionaries and sparse codes probabilistically to improve robustness with small training data. Experimental results show it outperforms state-of-the-art methods on various human activities.
A Bayesian framework for 3D human pose estimation from monocular images based on sparse representation (SR) is introduced. Our probabilistic approach aims at simultaneously learning two overcomplete dictionaries (one for the visual input space and the other for the pose space) with a shared sparse representation. Existing SR-based pose estimation approaches only offer a point estimation of the dictionary and the sparse codes. Therefore, they might be unreliable when the number of training examples is small. Our Bayesian framework estimates a posterior distribution for the sparse codes and the dictionaries from labeled training data. Hence, it is robust to overfitting on small-size training data. Experimental results on various human activities show that the proposed method is superior to the state of-the-art pose estimation algorithms.