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

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.