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|Title:||Pair-activity classification by bi-trajectories analysis|
|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|
|Appears in Collections:||Staff Publications|
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