Cross-camera knowledge transfer for multiview people counting.
IEEE Trans Image Process
; 24(1): 80-93, 2015 Jan.
Article
en En
| MEDLINE
| ID: mdl-25330489
ABSTRACT
We present a novel two-pass framework for counting the number of people in an environment, where multiple cameras provide different views of the subjects. By exploiting the complementary information captured by the cameras, we can transfer knowledge between the cameras to address the difficulties of people counting and improve the performance. The contribution of this paper is threefold. First, normalizing the perspective of visual features and estimating the size of a crowd are highly correlated tasks. Hence, we treat them as a joint learning problem. The derived counting model is scalable and it provides more accurate results than existing approaches. Second, we introduce an algorithm that matches groups of pedestrians in images captured by different cameras. The results provide a common domain for knowledge transfer, so we can work with multiple cameras without worrying about their differences. Third, the proposed counting system is comprised of a pair of collaborative regressors. The first one determines the people count based on features extracted from intracamera visual information, whereas the second calculates the residual by considering the conflicts between intercamera predictions. The two regressors are elegantly coupled and provide an accurate people counting system. The results of experiments in various settings show that, overall, our approach outperforms comparable baseline methods. The significant performance improvement demonstrates the effectiveness of our two-pass regression framework.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
/
Biometría
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
IEEE Trans Image Process
Asunto de la revista:
INFORMATICA MEDICA
Año:
2015
Tipo del documento:
Article