Human Action Recognition without Human
This work addresses the problem of over-reliance on human features in action recognition for computer vision researchers, but it is incremental as it builds on known issues with background features.
The paper investigates whether background sequences alone can classify human actions in large-scale datasets like UCF101, and experiments show that background sequences have a significant effect on understanding action labels.
The objective of this paper is to evaluate "human action recognition without human". Motion representation is frequently discussed in human action recognition. We have examined several sophisticated options, such as dense trajectories (DT) and the two-stream convolutional neural network (CNN). However, some features from the background could be too strong, as shown in some recent studies on human action recognition. Therefore, we considered whether a background sequence alone can classify human actions in current large-scale action datasets (e.g., UCF101). In this paper, we propose a novel concept for human action analysis that is named "human action recognition without human". An experiment clearly shows the effect of a background sequence for understanding an action label.