People counting with image retrieval using compressed sensing
Homa Foroughi, Nilanjan Ray, Hong Zhang · 2014
The estimation of the number of people present in an image has many applications such as intelligent transportation, urban planning and crowd surveillance. Rather than conventional counting by detection or regression/machine-learning methods, we propose an image retrieval approach, which uses an image descriptor to estimate the people count. We review the performance of several image descriptors. In addition, we propose a straightforward global image descriptor for image retrieval based on compressed sensing theory. Extensive evaluations on existing crowd analysis benchmark datasets demonstrate the effectiveness of our image retrieval-based approach compared to state-of-the-art regression-based people counting methods.