Efficient face detection based crowd density estimation using convolutional neural networks and an improved sliding window strategy - Kian Ara, Rouhollah; Matiolanski, Andrzej; Grega, Michał; Dziech, Andrzej; Baran, Remigiusz (Zielona Góra: Uniwersytet Zielonogórski)
Counting and detecting occluded faces in a crowd is a challenging task in computer vision. In this paper, we propose a new approach to face detection-based crowd estimation under significant occlusion and head posture variations. Most state-of-the-art face detectors cannot detect excessively occluded faces. To address the problem, an improved approach to training various detectors is described. To obtain a reasonable evaluation of our solution, we trained and tested the model on our substantially occluded data set. The dataset contains images with up to 90 degrees out-of-plane rotation and faces with 25%, 50%, and 75% occlusion levels.
Keywords: crowd density; face detection; head pose variations; various lighting conditions; occlusion