Temporal Dropout of Changes Approach to Convolutional Learning of Spatio-Temporal Features

The paper addresses the problem of learning features that can account for temporal dynamics present in videos. Although deep convolutional learning methods revolutionized several areas of multimedia and computer vision, there have been relatively few proposals dealing with ways in which these methods can be enabled to make use of motion information, critical to the extraction of useful information from video. We propose a temporal dropout of changes approach for this, which allows us to consider temporal information over a series of frames without increasing the number of training parameters of the network. To illustrate the potential of the proposed methodology, we focus on the problem of dynamic texture classification. Dynamic textures represent an important form of dynamics present in video data, so far not considered within the framework of deep learning. Initial results presented in the paper show that the proposed approach, based on a well-known deep convolutional neural network,...