Pair-activity classification by bi-trajectories analysis

Yue Zhou, Shuicheng Yan, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we address the pair-activity classification problem, which explores the relationship between two active objects based on their motion information. Our contributions are three-fold. First, we design a set of features, e.g., causality ratio and feedback ratio based on the Granger Causality Test (GCT), for describing the pair-activities encoded as trajectory pairs. These features along with conventional velocity and position features are essentially of multi-modalities, and may be greatly different in scale and importance. To make full use of them, we then present a novel feature normalization procedure to learn the coefficients for weighting these features by maximizing the discriminating power measured by weighted correlation. Finally, we collected a pair-activity database of five categories, each of which consists of about 170 instances. The extensive experiments on this database validate the effectiveness of the designed features for pair-activity representation, and also demonstrate that the proposed feature normalization procedure greatly boosts the pair-activity classification accuracy.

Original languageEnglish (US)
Title of host publication26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
DOIs
StatePublished - Sep 23 2008
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR - Anchorage, AK, United States
Duration: Jun 23 2008Jun 28 2008

Publication series

Name26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Other

Other26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
Country/TerritoryUnited States
CityAnchorage, AK
Period6/23/086/28/08

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering

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