Particle swarm optimization with deep learning for human action recognition

A novel method for human action recognition using a deep learning network with features optimized using particle swarm optimization is proposed. The binary histogram, Harris corner points and wavelet coefficients are the features extracted from the spatiotemporal volume of the video sequence. In order to reduce the computational complexity of the system, the feature space is reduced by particle swarm optimization technique with the multi-objective fitness function. Finally, the performance of the system is evaluated using deep learning neural network (DLNN). Two autoencoders are trained independently and the knowledge embedded in the autoencoders are transferred to the proposed DLNN for human action recognition. The proposed framework achieves an average recognition rate of 91% on UT interaction set 1, 88% on UT interaction set 2, 91% on SBU interaction dataset and 94% on Weizmann dataset.