Detection of groups in crowd considering their activity state
| Autor: | Tsukasa Ono, Noboru Babaguchi, Kazuaki Nakamura |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Group (mathematics)
business.industry 020207 software engineering 02 engineering and technology State (functional analysis) Variation (game tree) Machine learning computer.software_genre Support vector machine Hidden variable theory 0202 electrical engineering electronic engineering information engineering Probability distribution 020201 artificial intelligence & image processing Artificial intelligence Focus (optics) business Set (psychology) computer Mathematics |
| Zdroj: | ICPR |
| DOI: | 10.1109/icpr.2016.7899646 |
| Popis: | In this paper, we focus on the problem of group detection in crowd, which is a task of partitioning a set of pedestrians in a scene into small subsets called groups based on their trajectories. Most of previous methods use only a single model for representing a relationship between trajectories of pedestrians who belong to the same group. However, such relationship would vary depending on the activity state (e.g. walking together, approaching, splitting, and so on) of the group. In this paper, we propose a novel group detection method which can cope with a variation of groups' activity state. The proposed method constructs different models for each activity state in order to appropriately evaluate the relationship of pedestrians' trajectories. In addition, our method regards groups' activity state as hidden variables and estimates their probability distributions, which is used for integrating the constructed models. The proposed method outperforms existing methods in the experiment on the public dataset. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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