期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 15, 期 12, 页码 6334-6343出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2916671
关键词
Trajectory; Informatics; Motion measurement; Coherence; Sea measurements; Videos; Active learning; crowd analysis
类别
资金
- National Natural Science Foundation of China [61702104, 61877010, 61603096, 11501114]
- Natural Science Foundation of Fujian Province [2016J05155]
In this paper, we propose an approach to estimate and quantify the degree of extraversion for crowd motion based on individual trajectories. Extraversion is a typical personality that is often observed in human behaviors. We present a composite motion descriptor, which integrates the basic motion information and social metrics, to describe the extraversion of each individual in a crowd. In order to train a universal scoring function that can measure the degrees of extraversion, we incorporate the active learning technique with the relative attribute approach based on the social grouping behavior in crowd motions. In addition, we demonstrate the performance of the proposed method by measuring the degree of extraversion for real individual trajectories in a crowd and analyzing crowd scenes from a real-world dataset.
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