Comparative Analysis of CNN-based Spatiotemporal Reasoning in Videos

Comparative Analysis of CNN-based Spatiotemporal Reasoning in Videos

Abstract

Understanding actions and gestures in video streams requires temporal reasoning of the spatial content from different time instants, i.e., spatiotemporal (ST) modeling. In this paper, we have made a comparative analysis of different ST modeling techniques. Since convolutional neural networks (CNNs) are proved to be an effective tool as a feature extractor for static images, we apply ST modeling techniques on the features of static images from different time instants extracted by CNNs. All techniques are trained end-to-end together with a CNN feature extraction part and evaluated on two publicly available benchmarks: The Jester and the Something-Something dataset. The Jester dataset contains various dynamic and static hand gestures, whereas the Something-Something dataset contains actions of human-object interactions. The common characteristic of these two benchmarks is that the designed architectures need to capture the full temporal content of the actions/gestures in the correct order. Contrary to expectations, experimental results show that recurrent neural network (RNN) based ST modeling techniques yield inferior results compared to other techniques such as fully convolutional architectures. Codes and pretrained models of this work are publicly available.