Conference/Proceedings10th IEEE International Conference on Advanced Video and Signal-Based Surveillance
Start date27.08.2013
End date30.08.2013
AddressKraków, Polen
Author(s)Volker Eiselein, Hajer Fradi, Ivo Keller, Thomas Sikora, Jean-Luc Dugelay
TitleEnhancing Human Detection using Crowd Density Measures and an adaptive Correction Filter
AbstractIn this paper we present a method of improving a human detector by means of crowd density information. Human detection is especially challenging in crowded scenes which makes it important to introduce additional knowledge into the detection process. We compute crowd density maps in order to estimate the spatial distribution of people in the scene and show how it is possible to enhance the detection results of a state-of-the-art human detector by this information. The proposed method applies a self-adaptive, dynamic parametrization and as an additional contribution uses scene-adaptive learning of the human aspect ratio in order to reduce false positive detections in crowded areas. We evaluate our method on videos from different datasets and demonstrate how our system achieves better results than the baseline algorithm.
Key wordsmultimedia analysis, object, detection, histogram, oriented, gaussians, crowd, density, map