Tracking of multiple people in crowds using laser range scanners
| Autor: | Bolircene Marc, Jean-Michel Auberlet, Ladji Adiaviakoye, Plainchault Patrick |
|---|---|
| Přispěvatelé: | ESEO-GSII (GSII), ESEO-Tech, Université Bretagne Loire (UBL)-Université Bretagne Loire (UBL), Université Bretagne Loire (UBL), Laboratoire d'Ingéniérie des Systèmes Automatisés (LISA), Centre National de la Recherche Scientifique (CNRS)-Université d'Angers (UA), Laboratoire Exploitation, Perception, Simulateurs et Simulations (IFSTTAR/COSYS/LEPSIS), Communauté Université Paris-Est-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR) |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
[SPI.OTHER]Engineering Sciences [physics]/Other
0209 industrial biotechnology business.industry Computer science Association (object-oriented programming) Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Tracking (particle physics) Track (rail transport) LOCALISATION PIETON 020901 industrial engineering & automation Crowds 11. Sustainability 0202 electrical engineering electronic engineering information engineering Trajectory Range (statistics) 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Variable number SCANNER LASER |
| Zdroj: | International Conference on Intelligent Sensors, Sensor Networks and Information Processing-IEEE ISSNIP International Conference on Intelligent Sensors, Sensor Networks and Information Processing-IEEE ISSNIP, Apr 2014, SINGAPOUR, France. 7p, ⟨10.1109/ISSNIP.2014.6827668⟩ |
| Popis: | International Conference on Intelligent Sensors, Sensor Networks and Information Processing - IEEE ISSNIP, SINGAPOUR, SINGAPOUR, 21-/04/2014 - 24/04/2014; In everyday life, we can see amazing choreographies of movements of crowds of pedestrians. Pedestrians run into and avoid each other but do not seem to consciously cooperate. In this paper, we track a crowd of pedestrians in a large covered and cluttered area to understand their social behavior. Additionally, we try to analyze the characteristics of crowds of pedestrians such as traffic density, velocity, and trajectory. We introduce a stable feature extraction method based on accumulated distribution of successive laser frames. To isolate pedestrians, we propose a nonparametric method exploiting the Parzen windowing technique. We apply the new method of Rao-Blackwellized Monte Carlo data association to track a highly variable number of pedestrians. The algorithm is quantitatively evaluated through a social behavior experiment taking place in the lobby of a school. During this experiment, nearly 300 students are tracked. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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