Improved Shark Smell Optimization Algorithm for Human Action Recognition

Human Action Recognition (HAR) in uncontrolled environments targets to recognition of different actions from a video. An effective HAR model can be employed for an application like human-computer interaction, health care, person tracking, and video surveillance. Machine Learning (ML) approaches, specifically, Convolutional Neural Network (CNN) models had been widely used and achieved impressive results through feature fusion. The accuracy and effectiveness of these models continue to be the biggest challenge in this field. In this article, a novel feature optimization algorithm, called improved Shark Smell Optimization (iSSO) is proposed to reduce the redundancy of extracted features. This proposed technique is inspired by the behavior of white sharks, and how they find the best prey in the whole search space. The proposed iSSO algorithm divides the Feature Vector (FV) into subparts, where a search is conducted to find optimal local features from each subpart of FV. Once local optimal features are selected, a global search is conducted to further optimize these features. The proposed iSSO algorithm is employed on nine (9) selected CNN models. These CNN models are selected based on their top-1 and top-5 accuracy in ImageNet competition. To evaluate the model, two publicly available datasets UCF-Sports and Hollywood2 are selected.