Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion
- 주제(키워드) human action recognition , deep learning , features fusion , features selection , recognition
- 주제(기타) Chemistry, Analytical
- 주제(기타) Engineering, Electrical & Electronic
- 주제(기타) Instruments & Instrumentation
- 설명문(일반) [Khan, Seemab; Khan, Muhammad Attique] HITEC Univ Taxila, Dept Comp Sci, Txila 47080, Pakistan; [Alhaisoni, Majed] Univ Hail, Coll Comp Sci & Engn, Hail 55211, Saudi Arabia; [Tariq, Usman] Prince Sattam Bin Abdulaziz Univ, Coll Comp Engn & Sci, Al Kharaj 11942, Saudi Arabia; [Yong, Hwan-Seung] Ewha Womans Univ, Dept Comp Sci Engn, Seoul 120750, South Korea; [Armghan, Ammar; Alenezi, Fayadh] Jouf Univ, Dept Elect Engn, Coll Engn, Sakakah 72311, Saudi Arabia
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- OA유형 Green Published, gold
- 발행기관 MDPI
- 발행년도 2021
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000191059
- 본문언어 영어
- Published As https://doi.org/10.3390/s21237941
- PubMed https://pubmed.ncbi.nlm.nih.gov/34883944
초록/요약
Human action recognition (HAR) has gained significant attention recently as it can be adopted for a smart surveillance system in Multimedia. However, HAR is a challenging task because of the variety of human actions in daily life. Various solutions based on computer vision (CV) have been proposed in the literature which did not prove to be successful due to large video sequences which need to be processed in surveillance systems. The problem exacerbates in the presence of multi-view cameras. Recently, the development of deep learning (DL)-based systems has shown significant success for HAR even for multi-view camera systems. In this research work, a DL-based design is proposed for HAR. The proposed design consists of multiple steps including feature mapping, feature fusion and feature selection. For the initial feature mapping step, two pre-trained models are considered, such as DenseNet201 and InceptionV3. Later, the extracted deep features are fused using the Serial based Extended (SbE) approach. Later on, the best features are selected using Kurtosis-controlled Weighted KNN. The selected features are classified using several supervised learning algorithms. To show the efficacy of the proposed design, we used several datasets, such as KTH, IXMAS, WVU, and Hollywood. Experimental results showed that the proposed design achieved accuracies of 99.3%, 97.4%, 99.8%, and 99.9%, respectively, on these datasets. Furthermore, the feature selection step performed better in terms of computational time compared with the state-of-the-art.
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