Narrow Results

The process of utilizing Artificial Intelligence (AI) to identify and label human behaviors from unprocessed activity data gathered from various sources is known as Human Activity Recognition (HAR). Because of its potential applications across multiple areas, computer vision faces a significant challenge in recognizing human actions and the accompanying interactions with objects and the environment. Investigating the temporal and geographical characteristics of the skeleton sequence is essential for this endeavor, according to recent studies. However, efficiently extracting discriminative temporal and spatial information remains a difficult task. This work proposes a novel Human Action Recognition Model exploiting improved Graph Convolutional Network (GCN)-based pose estimation with a Hybrid Classifier (IGCN-HC). The phases carried out in this model are pre-processing, pose estimation, feature extraction, and activity recognition. Initially, the input video will be pre-processed and a frame from the input video stream will be generated. Subsequently, human pose estimation exploiting improved GCN will be accomplished. Further, human skeletal joints’ coordinates in two- or three-dimensional spaces are determined via human pose estimation. Then, Shape Local Binary Texture (SLBT) and an improved hierarchy of skeleton features have been used to detect the variance in different activities. In the last phase, a hybrid classification model with the combination of Deep Maxout and customized CNN has been proposed for the recognition phase. The model utilizes two inputs pose estimation results (skeleton) and the extracted features for training purposes. Finally, the proposed trained model is evaluated for recognition on different test inputs and contrasted with the existing techniques.

A number of authors have focused on this study to examine how huge data are perceived. A novel big data classification paradigm is introduced by the work’s preprocessing, feature extraction and classification techniques. Data normalization is carried out at the preprocessing stage. The MapReduce framework is then utilized to manage the massive data. Statistical features (mean, median, min/max and SD), higher-order statistical features (skewness, kurtosis and enhanced entropy), and correlation-based features are all extracted prior to classification. The Bi-LSTM and deep maxout hybrid classification model classifies the data during the reduction stage. To assure classification accuracy, training will also be deployed by the new Hybrid Butterfly Positioned Coot Optimization (HBPCO) algorithm. The proposed method’s accuracy of 97.45% beats the methods of NN (85.13%), CNN (83.78%), RNN (78.37%), Bi-LSTM (82.43%) and SVM (87.83%).