An Efficient Feature Selection Method for Video-Based Activity Recognition Systems

Abstract

Human activity recognition (HAR) is the examination of gestures and actions of humans from various resources such as depth or RGB cameras. In this work, we have designed a dynamic and robust feature selection algorithm for a HAR system, through which the system accurately recognizes various kinds of activities. In the proposed approach, we employed mutual information algorithm, which selects the prominent features from the extracted features. The proposed algorithm is the expansion of two methods like max-relevance and min-redundancy, respectively. This method has the capability to gather the assets of various extraction algorithms. But the procedure of selection may be unfair due to the dissimilarity between the classification power and redundancy of the features. To resolve this type of unfair selection, we stabilize both parts through the proposed algorithm that has autonomous upper limit of the mutual information function. Likewise, for the feature extraction and recognition, we used the symlet wavelet transform and hidden Markov model, respectively, for action classification. The proposed algorithm has been justified on depth-based database which has thirteen kinds of activities under comprehensive set of experiments. We showed that the proposed feature selection method achieved best classification accuracy against existing works.

Ambient assisted living for frail people through human activity recognition: state-of-the-art, challenges and future directions

“…The features are ordered according to the ranking of importance (computed with suitable score metrics) and those below a certain threshold are removed. Among the different algorithms, the most used are: ReliefF, statistical techniques such as Principal Component Analysis, Independent Component Analysis, Neighborhood Component Analysis and Correlation Based filter ( Suto et al, 2016 ; Alzahrani et al, 2019 ; Siddiqi and Alsirhani, 2022 ). Wrapper method selects the optimal features subset evaluating alternative sets by running the classification algorithm on the training data.…”