Time and Frequency Network for Human Action Detection in Videos
This work addresses action detection for video analysis, offering an incremental improvement by incorporating frequency features alongside existing spatiotemporal methods.
The paper tackles human action detection in videos by proposing TFNet, an end-to-end network that simultaneously extracts time and frequency features, achieving remarkable performance on JHMDB51-21 and UCF101-24 datasets as measured by frame-mAP.
Currently, spatiotemporal features are embraced by most deep learning approaches for human action detection in videos, however, they neglect the important features in frequency domain. In this work, we propose an end-to-end network that considers the time and frequency features simultaneously, named TFNet. TFNet holds two branches, one is time branch formed of three-dimensional convolutional neural network(3D-CNN), which takes the image sequence as input to extract time features; and the other is frequency branch, extracting frequency features through two-dimensional convolutional neural network(2D-CNN) from DCT coefficients. Finally, to obtain the action patterns, these two features are deeply fused under the attention mechanism. Experimental results on the JHMDB51-21 and UCF101-24 datasets demonstrate that our approach achieves remarkable performance for frame-mAP.