Optimized Three-Fold Deep Learning Model and ROI Identification for Video-Based Human Activity Recognition

Abstract

A novel to address these issues, human activity recognition is developing a three-fold deep learning model. Prior to anything else, the collected raw video frames undergo pre-processing. Wiener filtering, video-to-frame conversion, and contrast enhancement based on contrast limited adaptive histogram equalization are among the activities accomplished during this phase. Characteristics including monogenic binary coding, binary pattern of phase congruency, local Gabor transitional pattern, and chessboard median binary pattern are then recovered from the ROI region that was obtained. Using artificial ecosystem customized bald eagle optimization, the best features from the retrieved features would be selected. In the activity categorization phase, the three-fold deep learning model is trained using the chosen optimal features. Three deep learning models are used to model the activity categorization phase: convolutional neural network (CNN), optimized recurrent neural network (optimized RNN), and bidirectional long short-term memory (Bi-LSTM). With ratings of 94.3%, 94.39%, 92.2%, and 94.03% for 60, 70, 80, and 90 learning percentages, accordingly, the suggested model has shown the maximum detection accuracy. The two types of datasets used are Action Recognition Data Set, Human Action Clips, and Segments Dataset for Recognition and Temporal Localization. Real-time performance may suffer from the drawn-out frame-by-frame video processing and feature extraction procedure. In order to demonstrate the effectiveness of the recommended approach, the efficiency of the suggested approach is finally compared to other conventional models.