Human Action Recognition Using a Modified Convolutional Neural Network

Authors:
Ho-Joon Kim;Joseph S. Lee;Hyun-Seung Yang
Affiliations:
School of Computer Science and Electronic Engineering, Handong University, Pohang, 791-708, Korea;School of Computer Science and Electronic Engineering, Handong University, Pohang, 791-708, Korea;Department of Computer Science, KAIST, Daejeon, 305-701, Korea
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

In this paper, a human action recognition method using a hybrid neural network is presented. The method consists of three stages: preprocessing, feature extraction, and pattern classification. For feature extraction, we propose a modified convolutional neural network (CNN) which has a three-dimensional receptive field. The CNN generates a set of feature maps from the action descriptors which are derived from a spatiotemporal volume. A weighted fuzzy min-max (WFMM) neural network is used for the pattern classification stage. We introduce a feature selection technique using the WFMM model to reduce the dimensionality of the feature space. Two kinds of relevance factors between features and pattern classes are defined to analyze the salient features.