Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPR.2008.4587816
Title: Pair-activity classification by bi-trajectories analysis
Authors: Zhou, Y.
Yan, S. 
Huang, T.S.
Issue Date: 2008
Source: Zhou, Y.,Yan, S.,Huang, T.S. (2008). Pair-activity classification by bi-trajectories analysis. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR : -. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2008.4587816
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. ©2008 IEEE.
Source Title: 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
URI: http://scholarbank.nus.edu.sg/handle/10635/71355
ISBN: 9781424422432
DOI: 10.1109/CVPR.2008.4587816
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