Video-Based Human Activity Recognition Using Multilevel Wavelet Decomposition and Stepwise Linear Discriminant Analysis

• Published in: Sensors (Basel, Switzerland) Vol. 14; no. 4; pp. 6370 - 6392
• Main Authors: Siddiqi, Muhammad, Ali, Rahman, Rana, Md, Hong, Een-Kee, Kim, Eun, Lee, Sungyoung
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 04.04.2014; MDPI
• Subjects: activity recognition; Algorithms; Databases as Topic; Datasets; Decomposition; Discriminant Analysis; Electronic mail systems; Frames; hidden markov model; Human; Human Activities; Human motion motion; Humans; Markov Chains; Markov models; Mathematical models; Methods; Moving object recognition; Pattern recognition; Pattern Recognition, Automated - methods; Principal Component Analysis; Recognition; Sensors; stepwise linear discriminant analysis; Support vector machines; Wavelet; Wavelet Analysis; wavelet decomposition; Wavelet transforms
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s140406370

Video-based human activity recognition (HAR) means the analysis of motions and behaviors of human from the low level sensors. Over the last decade, automatic HAR is an exigent research area and is considered a significant concern in the field of computer vision and pattern recognition. In this paper, we have presented a robust and an accurate activity recognition system called WS-HAR that consists of wavelet transform coupled with stepwise linear discriminant analysis (SWLDA) followed by hidden Markov model (HMM). Symlet wavelet has been employed in order to extract the features from the activity frames. The most prominent features were selected by proposing a robust technique called stepwise linear discriminant analysis (SWLDA) that focuses on selecting the localized features from the activity frames and discriminating their class based on regression values (i.e., partial F-test values). Finally, we applied a well-known sequential classifier called hidden Markov model (HMM) to give the appropriate labels to the activities. In order to validate the performance of the WS-HAR, we utilized two publicly available standard datasets under two different experimental settings, n–fold cross validation scheme based on subjects; and a set of experiments was performed in order to show the effectiveness of each approach. The weighted average recognition rate for the WS-HAR was 97% across the two different datasets that is a significant improvement in classication accuracy compared to the existing well-known statistical and state-of-the-art methods.