Human action recognition using genetic algorithms and convolutional neural networks

作者：

Highlights：

• An 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

摘要

Highlights•An 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

论文关键词：Convolutional neural network (CNN),Genetic algorithms (GA),Human action recognition,Action bank features

论文评审过程：Received 30 August 2015, Revised 13 January 2016, Accepted 13 January 2016, Available online 23 January 2016, Version of Record 23 August 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.01.012