Abstract
Human action recognition (HAR) is an essential but challenging task for observing human movements. This problem encompasses the observations of variations in human movement and activity identification by machine learning algorithms. This article addresses the challenges in activity recognition by implementing and experimenting an intelligent segmentation, features reduction and selection framework. A novel approach has been introduced for the fusion of segmented frames and multi-level features of interests are extracted. An entropy-skewness based features reduction technique has been implemented and the reduced features are converted into a codebook by serial based fusion. A custom made genetic algorithm is implemented on the constructed features codebook in order to select the strong and wellknown features. The features are exploited by a multi-class SVM for action identification. Comprehensive experimental results are undertaken on four action datasets, namely, Weizmann, KTH, Muhavi, and WVU multi-view. We achieved the recognition rate of 96.80%, 100%, 100%, and 100% respectively. Analysis reveals that the proposed action recognition approach is efficient and well accurate as compare to existing approaches.