Abstract
Recognition of human actions from visual contents is a budding field of computer vision and image understanding. The problem with such a 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 named 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 very small fraction of the original feature vector.
Highlights
Human Action Recognition (HAR) plays an essential role in human-to-human interaction and many interpersonal relations by providing vital information about the identity of a person, their personality and psychological state, which are generally challenging to extract [1]
Coalition Game In Co-operative Genetic Algorithm (CGA), we have made an effort to reduce the computational complexity of calculating Shapley value and use that Shapley value to improve the solution of Enhanced GA (EGA)
After EGA produces the final population of chromosomes in an iteration, they are passed to coalition game for computation of Shapley values for the iteration
Summary
Human Action Recognition (HAR) plays an essential role in human-to-human interaction and many interpersonal relations by providing vital information about the identity of a person, their personality and psychological state, which are generally challenging to extract [1]. A new wrapper FS procedure named Co-operative Genetic Algorithm (CGA) is designed for the feature dimensionality reduction from a combination of four state-of-the-art feature descriptors; namely, Histograms of Oriented Gradients (HOG) [15], Gray Level Cooccurrence Matrix (GLCM) [16], Speeded Up Robust Features (SURF) [17] and GIST [18] These feature descriptors have been successfully applied for solving typical pattern recognition problems. We have considered them for extracting feature vectors which will serve as an input to our proposed FS model These feature descriptors are applied on four benchmark 2D RGB video datasets having different number of action classes such as Weizmann [19], KTH [20], UCF11 [21], HMDB51 [22].
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