Hybrid Feature Extraction and Knowledge Distillation Based Deep Learning Model for Human Activity Recognition System

This article introduces the Generative Adversarial Network (GAN) framework model, which uses offline knowledge distillation (KD) to move spatio-deep data from a large teacher to a smaller student model. To achieve this, the teacher model named EfficientNetB7 embedded with spatial attention (E2SA) and a multi-layer Gated Recurrent Unit (GRU) is used. A hybrid feature extraction method known as Completed Hybrid Local Binary Pattern (ChLBP) is employed prior to the prediction process. After feature extraction, the hybrid features are parallelly given as input to both teacher and student models. In the teacher model, E2SA extracts both deep and spatio attention activity features, and these features are then input to the multi-layer GRU, which learns the human activity frame sequences overall. The proposed model obtains 98.50 % recognition accuracy on the UCF101 dataset and 79.21 % recognition accuracy on the HMDB51 dataset, which is considerably better than the existing models.