Conteo de personas con un sensor RGBD comercial

• M. Castrillón-Santan ^[1] ; J. Lorenzo-Navarro ^[1] ; D. Hernández-Sosa ^[1]
  1. [1] Universidad de Las Palmas de Gran Canaria

     Universidad de Las Palmas de Gran Canaria

     Gran Canaria, España

     
• Localización: Revista iberoamericana de automática e informática industrial ( RIAI ), ISSN-e 1697-7920, Vol. 11, Nº. 3, 2014, págs. 348-357
• Idioma: español
• DOI: 10.1016/j.riai.2014.05.006
• Títulos paralelos:
  • People counting using a consumer RGBD camera
• Enlaces
  • Texto completo
• Resumen
  • español

    En este trabajo se demuestra que la información de profundidad proporcionada por una cámara RGBD comercial de bajo coste, es una fuente fiable de datos para realizar de forma robusta el conteo automático de personas. La adopción de una configuración de vista cenital reduce la complejidad del problema, al mismo tiempo que permite preservar la privacidad de las personas moni- torizadas. Para llevar a cabo el estudio experimental se han considerado dos técnicas propias del campo de análisis de imágenes 2D trasladadas al contexto de imágenes de profundidad. Las pruebas evaluaron su rendimiento con v??ıdeos reales sin restricciones de iluminación, incluyendo episodios de iluminación cambiante o muy baja. En este conjunto experimental se realizó la detección, seguimiento y análisis de patrones de comportamiento de las personas que cruzaban el campo de visión. Los resultados obtenidos alcanzan una tasa de acierto próxima al 95%, superando los obtenidos con técnicas actuales basadas exclusivamente en información visual. Estos resultados sugieren la utilidad del uso de información de profundidad en esta tarea particular.

  • English

    In this paper, we prove that depth information provided by a consumer depth camera is a reliable data source to perform ro- bust people counting. The adoption of a top view configuration reduces the space problem complexity for this task, while pre- serving privacy. Two different background subtraction approaches for color images are transferred to this context and tested in real video to perform detection, tracking, and behavioral pat- terns analysis of subjects crossing the field of view. The results achieved in an experimental setup with real video reported a TPR over 95%, beating robust GMM background subtraction based only on the visual cue. The results suggest the benefits of the depth cue for this particular task.

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