Fusion of histogram based features for Human Action Recognition

Recognition of human actions from visual contents is a budding field of computer vision and image understanding. The problem with such recognition system is the huge dimensions of the feature vectors. Many of these features are irrelevant to the classification mechanism. For this reason, in this paper, we propose a novel Feature Selection (FS) model called Cooperative Genetic Algorithm (CGA) to select some of the most important and discriminating features from the entire feature set to improve the classification accuracy as well as the time requirement of the activity recognition mechanism. In CGA, we have made an effort to embed the concepts of cooperative game theory in GA to create a both-way reinforcement mechanism to improve the solution of the FS model. The proposed FS model is tested on four benchmark video datasets such as Weizmann, KTH, UCF11, HMDB51 and two sensor-based UCI HAR datasets. The experiments are conducted using four state-of-the-art feature descriptors, namely HOG, GLCM, SURF, and GIST. It is found that there is a significant improvement in the overall classification accuracy while considering nearly 50 to 60% of the original feature vector.

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Linear dynamical systems approach for human action recognition with dual-stream deep features

Mukaidani

2021

Appl Intell

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Fusion of histogram based features for Human Action Recognition

Cited by 8 publications

References 10 publications

A Comparative Study of Different Feature Descriptors for Video-Based Human Action Recognition

A Comparative Study of Different Feature Descriptors for Video-Based Human Action Recognition

CGA: a new feature selection model for visual human action recognition

Linear dynamical systems approach for human action recognition with dual-stream deep features

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