Hybrid deep neural network model for human action recognition

• A hybrid deep neural network model for human action recognition using action bank features is proposed.
• Classifier diversity is achieved by diversifying the input features and varying the initialization of the weights of the neural network.
• The performance of the proposed approach using different fusion functions is analyzed.
• We examine changes in output of the proposed fusion model with increasing base classifiers, for all the test cases.

In this paper, we propose a hybrid deep neural network model for recognizing human actions in videos. A hybrid deep neural network model is designed by the fusion of homogeneous convolutional neural network (CNN) classifiers. The ensemble of classifiers is built by diversifying the input features and varying the initialization of the weights of the neural network. The convolutional neural network classifiers are trained to output a value of one, for the predicted class and a zero, for all the other classes. The outputs of the trained classifiers are considered as confidence value for prediction so that the predicted class will have a confidence value of approximately 1 and the rest of the classes will have a confidence value of approximately 0. The fusion function is computed as the maximum value of the outputs across all classifiers, to pick the correct class label during fusion. The effectiveness of the proposed approach is demonstrated on UCF50 dataset resulting in a high recognition accuracy of 99.68%.

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