Active learning for high-density crowd count regression

Jennifer Vandoni (1, 2) , Emanuel Aldea (1, 2) , Sylvie Le Hégarat-Mascle (1, 2)

1 SATIE-MOSS - Méthodes et Outils pour les Signaux et Systèmes
2 UP11 - Université Paris-Sud - Paris 11

Efficient crowd counting is an essential task in crowd monitoring, and significant advances have been made in this field recently by counting-by-regression techniques. We propose in this work a learning-to-count strategy with a generic detection algorithm which benefits from a counting regressor in order to identify crowded subregions with inadequate head detection performance, and to improve their representativeness in the training set. A straightforward but crucial step is proposed in order to take into account perspective correction within the proposed framework. An evaluation on Makkah images with medium to very high densities demonstrates the effectiveness of our algorithm and its capability to reach a count error of less than 5% in this difficult setting.

Domaines: Traitement du signal et de l'image [eess.SP]; Apprentissage [cs.LG]

Jennifer Vandoni, Emanuel Aldea, Sylvie Le Hégarat-Mascle. Active learning for high-density crowd count regression. 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Aug 2017, Lecce, Italy. ⟨10.1109/AVSS.2017.8078508⟩. ⟨hal-01691757⟩