A classification-regression deep learning model for people counting

Abstract

In this paper, we construct a multi-task deep learning model to simultaneously predict people number and the level of crowd density. Motivated by the success of applying “ambiguous labelling” to age estimation problem, we also manage to employ this strategy to the people counting problem. We show that it is a reasonable strategy since people counting problem is similar to the age estimation problem. Also, by applying “ambiguous labelling”, we are able to augment the size of training dataset, which is a desirable property when applying to deep learning model. In a series of experiment, we show that the “ambiguous labelling” strategy can not only improve the performance of deep learning but also enhance the prediction ability of traditional computer vision methods such as Random Projection Forest with hand-crafted features.