TORNADO: A Spatio-Temporal Convolutional Regression Network for Video Action Proposal
Hongyuan Zhu, Romain Vial, Shijian Lu · 2017
Given a video clip, action proposal aims to quickly generate a number of spatio-temporal tubes that enclose candidate human activities. Recently, the regression-based networks and long-term recurrent convolutional network (L-RCN) have demonstrated superior performance in object detection and action recognition. However, the regression-based detectors perform inference without considering the temporal context among neighboring frames, and the LRC-N using global visual percepts lacks the capability to capture local temporal dynamics. In this paper, we present a novel framework called TORNADO for human action proposal detection in un-trimmed video clips. Specifically, we propose a spatio-temporal convolutional network that combines the advantages of regression-based detector and L-RCN by empowering Convolutional LSTM with regression capability. Our approach consists of a temporal convolutional regression network (T-CRN) and a spatial regression network (S-CRN) which are trained end-to-end on both RGB and optical flow streams. They fuse appearance, motion and temporal contexts to regress the bounding boxes of candidate human actions simultaneously in 28 FPS. The action proposals are constructed by solving dynamic programming with peak trimming of the generated action boxes. Extensive experiments on the challenging UCF-101 and UCF-Sports datasets show that our method achieves superior performance as compared with the state-of-the-arts.