PRR-FCM-Based Segmentation Model for Human Activity Recognition: A Hybrid Deep Learning Strategy

Recognizing human activity is a challenging task in many applications like visual surveillance, Human–computer interaction, etc. Numerous machine learning (ML) and deep learning (DL) approaches are used to emulate the human behavior of everyday life, as they provide better recognition by learning complex features. This paper proposes an innovative human activity recognition framework with the following steps. Initially, the given video is converted into a set of frames and then applies median filtering to remove the noise for efficient recognition. Subsequently, the pixel-relatedness retrieval-fuzzy clustering means algorithm (PRR-FCM) approach is proposed for image segmentation, in which a pixel-relatedness retrieval strategy is used that considers only the proximity-related pixel for efficient segmentation. The retrieval of features is an important aspect, thereby this paper goes with the extraction of texture features, relative standard deviation induced in multi-texon (RSIMT), in which relative standard deviation is induced for interchanging the center pixel value. Also, the other features like the hierarchy of the skeleton and local binary pattern (LBP) are extracted from the segmented image. Further, the retrieved features are fed to the hybrid model that combines models like modified deep Maxout (MDM) and convolution neural network (CNN) models to recognize human activity efficiently. The modified exponential linear unit (ELU) activation function and Log loss function are the enhancing formulation of the MDM model to determine accurate recognition results.