Spatio-Temporal Action Detection with Cascade Proposal and Location Anticipation
This addresses the problem of accurately detecting human actions in videos for analyzing large-scale video data, but it is incremental as it builds on existing region proposal and consistency methods.
The paper tackles spatio-temporal action detection in untrimmed videos by proposing a cascade proposal and location anticipation model, achieving state-of-the-art performance on UCF101 and LIRIS-HARL datasets.
In this work, we address the problem of spatio-temporal action detection in temporally untrimmed videos. It is an important and challenging task as finding accurate human actions in both temporal and spatial space is important for analyzing large-scale video data. To tackle this problem, we propose a cascade proposal and location anticipation (CPLA) model for frame-level action detection. There are several salient points of our model: (1) a cascade region proposal network (casRPN) is adopted for action proposal generation and shows better localization accuracy compared with single region proposal network (RPN); (2) action spatio-temporal consistencies are exploited via a location anticipation network (LAN) and thus frame-level action detection is not conducted independently. Frame-level detections are then linked by solving an linking score maximization problem, and temporally trimmed into spatio-temporal action tubes. We demonstrate the effectiveness of our model on the challenging UCF101 and LIRIS-HARL datasets, both achieving state-of-the-art performance.