A composite framework of deep multiple view human joints feature extraction and selection strategy with hybrid adaptive sunflower optimization‐whale optimization algorithm for human action recognition in video sequences. (18th January 2022)

• Record Type: Journal Article
• Authors: Rajappan, Rajitha Jasmine; Kondampatti Kandaswamy, Thyagharajan
• Is Part Of: Computational intelligence. Volume 38:Number 2(2022)
• Page Start: 366
• Page End: 396
• Publication Date: 2022-01-18
• Subjects: adaptive sun flower optimization -- deep improved fully resolution convolutional neural networks (DIFR‐CNN) -- feature selection -- multiple view -- video‐based HAR; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12499
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: In computer vision and pattern recognition field, video‐based human action recognition (HAR) is the most predominant research area. Object recognition is needed to recognize the subjects regarding video contents, which allows reactive enquiry in a large number of camera contents, mainly in security based platforms where there is a prevalent growth of closed circuit television cameras. Generally, object detectors that have high performance are trained on a large collection of public benchmarks. Identifying human activities from unconstrained videos is the primary challenging task. Further, the feature extraction and feature selection from these unconstrained videos is also considered as a challenging issue. For that, in this article a new composite framework of HAR model is constructed by introducing an efficient feature extraction and selection strategy. The proposed feature extraction model extracts multiple view features, human joints features based on the domain knowledge of the action and fuses them with deep high level features extracted by an improved fully resolution convolutional neural networks. Also, it optimizes the feature selection strategy using the hybrid whale optimization algorithm and adaptive sun flower optimization that maximizes the feature entropy, correlation. It minimizes the error rate for improving the recognition accuracy of the proposed composite framework. The proposed model is validated on four different datasets, namely, Olympics sports, Virat Release 2.0, HMDB51, and UCF 50 sports action dataset to prove its effectiveness. The simulation results show that the proposed composite framework outperforms all the existing human recognition model in terms of classification accuracy and detection rate.