Activity recognition by image synthesis
We propose a vision-based method for recognizing first-person reading activity with deep learning. For the success of deep learning, it is well known that a large amount of training data plays a vital role. Unlike image classification, there are less publicly available datasets for reading activity recognition, and the collection of book images might cause copyright trouble. In this paper, we develop a synthetic approach for generating positive training images. Our approach synthesizes computer-generated images and real backround images. In experiments, we show that this synthesis is effective in combination with pre-trained deep convolutional neural networks and also our trained neural network outperforms other baselines.
- Yuta Segawa, Kazuhiko Kawamoto, and Kazushi Okamoto, First-person reading activity recognition by deep learning with synthetically generated images, EURASIP Journal on Image and Video Processing, vol.2018-33, 13 pages, 2018.