Pair-activity classification by bi-trajectories analysis

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 pairactivities 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 al...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Yue Zhou, Shuicheng Yan, Thomas S. Huang
• Keywords: Causality Ratio, Computer Vision, CVPR 2008, Feature Normalization Procedure, Granger Causality Test, Pair-activity Classification Accuracy, Pair-activity Classification Problem