First-person reading activity recognition by deep learning with synthetically generated images

With the development of wearable cameras, first-person activity recognition has been a popular topic in recent years [1]. There are many conventional approaches which tackle first-person activity recognition. Some of these approaches employ motion feature such as optical flow and also a classifier, e.g., LogitBoost and SVM (support vector machine)[2, 3]. In recent years, DCNN (deep convolutional neural network), the state-of-the-art model for visual recognition, has been proposed [4] and then applied to several tasks on first-person activity recognition. Although DCNN models provide remarkable results for image recognition, they require a large amount of labeled training samples.