Title
Spatiotemporal Deformable Part Models For Action Detection
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 sub volumes 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. © 2013 IEEE.
Publication Date
11-15-2013
Publication Title
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Number of Pages
2642-2649
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/CVPR.2013.341
Copyright Status
Unknown
Socpus ID
84887356306 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84887356306
STARS Citation
Tian, Yicong; Sukthankar, Rahul; and Shah, Mubarak, "Spatiotemporal Deformable Part Models For Action Detection" (2013). Scopus Export 2010-2014. 6468.
https://stars.library.ucf.edu/scopus2010/6468