Quantum behaved Intelligent Variant of Gravitational Search Algorithm with Deep Neural Networks for Human Activity Recognition

Abstract

Human activity recognition (HAR) encompasses the detection of daily routine activities to advance usability in detecting crime and preventing dangerous activities. The recognition of activities from videos and image sequences with higher exactitude is a major challenge due to system complexities. The efficient feature optimization approach can reduce system complexities by removing ineffective features, which also improves the activity recognition performance. This research work presents a novel quantum behaved intelligent gravitational search algorithm to optimize the features for human activity recognition. The proposed intelligent variant is termed as INQGSA, which optimizes the features by using the advantageous attributes of quantum computing (QC) and intelligent gravitational search algorithm (INGSA). In INQGSA, the intelligent factor avoids the trapping of mass agents in later iterations by using the information of the best and worst agents to update the position of agents. The addition of quantum computing based attributes (such as quantum bits, their superposition, and quantum gates, etc.) ensures a better diversity of discrete optimized features. To analyze the superiority of INQGSA, the feature optimization is also conducted with the gravitational search algorithm (GSA) and the quantum-inspired binary gravitational search algorithm (QBGSA). Finally, the optimized selected features are utilized by the deep neural networks (DNN) of ResNet-50V2 and ResNet-101V2 for the classification of activities. The activity recognition experiments are conducted on the UCF101 and HMDB51 datasets. The performance comparison of the proposed HAR system with state-of-the-art techniques signifies that the proposed system is superior and effective in detecting the different activities.