Evidential query-by-committee active learning for pedestrian detection in high-density crowds

Jennifer Vandoni (1) , Emanuel Aldea (1, 2) , Sylvie Le Hégarat-Mascle (3)

1 SATIE-MOSS - Méthodes et Outils pour les Signaux et Systèmes
2 UP11 - Université Paris-Sud - Paris 11
3 SATIE - Systèmes et Applications des Technologies de l'Information et de l'Energie

The automatic detection of pedestrians in dense crowds has become recently a very active topic of research due to the implications for public safety, and also due to the increased frequency of large scale social events. The detection task is complicated by multiple factors such as strong occlusions, high homogeneity, small target size, etc., and different types of detectors are able to provide complementary interpretations of the input data, with varying individual levels of performance. Our first contribution consists in outlining a fusion strategy under the form of an ensemble method, which models the imprecision arising from each of the detectors, both in the calibration and in the spatial domains in an evidential framework. Then, we propose a sample selection for augmenting the training set used jointly by the committee of classifiers, based on evidential disagreement measures among the base members in a Query-by-Committee context. The results show that the proposed fusion algorithm is effective in exploiting the strengths of the individual classifiers, as well as in augmenting the training set with informative samples which allow the resulting detector to enhance its performance.

Mots clés: Pedestrian detection; Crowd analysis; Ensemble methods; Belief function theory; Active learning

Domaines: Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Jennifer Vandoni, Emanuel Aldea, Sylvie Le Hégarat-Mascle. Evidential query-by-committee active learning for pedestrian detection in high-density crowds. International Journal of Approximate Reasoning, 2018, 104, pp.166-184. ⟨10.1016/j.ijar.2018.11.007⟩. ⟨hal-01918406⟩