Comparison of Classifiers for Human Activity Recognition
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Efficient human action and gait analysis using multiresolution motion energy histogram
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Gait-based action recognition via accelerated minimum incremental coding length classifier
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
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In this paper, we present a novel approach based on gait energy image (GEI) and co-evolutionary genetic programming (CGP) for human activity classification. Specifically, Hu's moment and normalized histogram bins are extracted from the original GEIs as input features. CGP is employed to reduce the feature dimensionality and learn the classifiers. The strategy of majority voting is applied to the CGP to improve the overall performance in consideration of the diversification of genetic programming. This learningbased approach improves the classification accuracy by approximately 7 percent in comparison to the traditional classifiers.