Enhancing human detection using crowd density measures and an adaptive correction filter

Eiselein, Volker; Fradi, Hajer; Keller, Ivo; Sikora, Thomas; Dugelay, Jean-Luc
AVSS 2013, 10th IEEE International Conference on Advanced Video and Signal-Based Surveillance, August 27-30, 2013, Krakow, Poland

In this paper we improve 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 using 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.

Sécurité numérique
Eurecom Ref:
© 2013 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PERMALINK : https://www.eurecom.fr/publication/4046