Human action recognition using genetic algorithms and convolutional neural networks
In this paper, an approach for human action recognition using genetic algorithms (GA) and deep convolutional neural networks (CNN) is proposed. We demonstrate that initializing the weights of a convolutional neural network (CNN) classifier based on solutions generated by genetic algorithms (GA) minimizes the classification error. A gradient descent algorithm is used to train the CNN classifiers (to find a local minimum) during fitness evaluations of GA chromosomes. The global search capabilities of genetic algorithms and the local search ability of gradient descent algorithm are exploited to find a solution that is closer to global optimum. We show that combining the evidences of classifiers generated using genetic algorithms helps to improve the performance. We demonstrate the efficacy of the proposed classification system for human action recognition on UCF50 dataset. HighlightsAn approach for human action recognition using genetic algorithms (GA) and deep convolutional neural networks (CNN) is proposed.The global and local search capabilities of genetic algorithms and gradient descent algorithms, respectively, are exploited by initializing the CNN classifier with the solutions generated by genetic algorithms and training the classifiers using gradient descent algorithm for fitness evaluation of GA chromosomes.Also, the evolution of candidate solutions explored by GA framework is examined.A near accurate recognition performance of 99.98
Pattern Recognit.