An Automatic Design of Factors in a Human-Pose Estimation System Using Neural Networks

Previous studies on human-pose estimation (HPE) rely on the design of factors to represent underlying probability distributions that model human poses. However, designing those factors manually is laborious. Moreover, manually designed factors might not represent underlying probability distributions properly. In this paper, we utilize feedforward neural networks (NNs) to design factors of our previous work on HPE and build an NN-based HPE system. We first propose a mapping that converts a Bayesian network to a feedforward NN. Then, the system is built based on the proposed mapping that consists of two steps: 1) structure identification and 2) parameter learning. In the structure identification, we develop a bottom-up approach to build a feedforward NN while preserving a Bayesian-network structure. In the parameter learning, we create a part-based approach to learn synaptic weights by decomposing a feedforward NN into parts. Using the proposed mapping, our previous work of an action-mixture model (AMM) for HPE is converted to a feedforward NN called NN-AMM. Based on the concept of distributed representation, NN-AMM is further modified to a scalable feedforward NN called NND-AMM. The NN-based HPE system is then built by using viewpoint-and-shape-feature-histogram features extracted from 3-D-point-cloud input and NND-AMM to estimate 3-D human poses. The results showed that the proposed mapping could design AMM factors automatically. NND-AMM could provide more accurate human-pose estimates with fewer hidden neurons than both AMM and NN-AMM could. Both NN-AMM and NND-AMM could adapt to different types of input, showing the adaptability of using feedforward NNs to design factors.