Time and Frequency Network for Human Action Detection in Videos

TL;DR

This paper introduces TFNet, an end-to-end deep learning model that simultaneously captures time and frequency domain features for improved human action detection in videos, leveraging 3D-CNN and 2D-CNN with attention-based fusion.

Contribution

The novel integration of time and frequency features in a unified network for human action detection, utilizing separate CNN branches and attention mechanisms.

Findings

Achieves superior frame-mAP on JHMDB51-21 dataset.

Outperforms existing methods on UCF101-24 dataset.

Demonstrates the effectiveness of frequency domain features in action detection.

Abstract

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.