TICNN: A Hierarchical Deep Learning Framework for Still Image Action Recognition Using Temporal Image Prediction

Lack of temporal information in still images is a major obstacle in still image action recognition. Here, we propose TICNN, a novel deep learning framework, addressing this challenge. We first introduce the concept of a Temporal Image, a compact representation of hypothetical sequence of images. We then take advantage of this concept to architect TICNN, composed of two convolutional neural networks. The first CNN learns a novel model to predict temporal images using transfer learning. Our second CNN extracts temporal image features to classify human actions in still images. Unlike previous efforts, TICNN learns temporal information, the missing cue, in a still image rather than merely focusing on spatial features. To the best of our knowledge, this is the first attempt to predict temporal images, dynamic patterns of a still image, to alleviate the lack of motion information in still images. Extensive experiments on four benchmarks demonstrate the positive effect of temporal image prediction on the accuracy of still image action recognition, outperforming the state-of-the-art methods in the literature.