Spatiotemporal Deformable Part Models for Action Detection
Venue
Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR 2013)
Publication Year
2013
Authors
Yicong Tian, Rahul Sukthankar, Mubarak Shah
BibTeX
Abstract
Deformable part models have achieved impressive performance for object detection,
even on difficult image datasets. This paper explores the generalization of
deformable part models from 2D images to 3D spatiotemporal volumes to better study
their effectiveness for action detection in video. Actions are treated as
spatiotemporal patterns and a deformable part model is generated for each action
from a collection of examples. For each action model, the most discriminative 3D
subvolumes are automatically selected as parts and the spatiotemporal relations
between their locations are learned. By focusing on the most distinctive parts of
each action, our models adapt to intra-class variation and show robustness to
clutter. Extensive experiments on several video datasets demonstrate the strength
of spatiotemporal DPMs for classifying and localizing actions.
