Max-margin structured output regression for spatio-temporal action localization
Date of Issue2012
Advances in Neural Information Processing Systems 25 (NIPS 2012)
School of Electrical and Electronic Engineering
Structured output learning has been successfully applied to object localization, where the mapping between an image and an object bounding box can be well captured. Its extension to action localization in videos, however, is much more challenging, because one needs to predict the locations of the action patterns both spatially and temporally, i.e., identifying a sequence of bounding boxes that track the action in video. The problem becomes intractable due to the exponentially large size of the structured video space where actions could occur. We propose a novel structured learning approach for spatio-temporal action localization. The mapping between a video and a spatio-temporal action trajectory is learned. The intractable inference and learning problems are addressed by leveraging an efficient Max-Path search method, thus makes it feasible to optimize the model over the whole structured space. Experiments on two challenging benchmark datasets show that our proposed method outperforms the state-of-the-art methods.
DRNTU::Engineering::Electrical and electronic engineering
© 2012 Massachusetts Institute of Technology Press. This paper was published in Advances in Neural Information Processing Systems 25 (NIPS 2012) and is made available as an electronic reprint (preprint) with permission of Massachusetts Institute of Technology Press. The paper can be found at the following official URL: [http://papers.nips.cc/paper/4794-max-margin-structured-output-regression-for-spatio-temporal-action-localization]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.