Joint Probabilistic People Detection in Overlapping Depth Images

• Main Authors: Johannes Wetzel, Astrid Laubenheimer, Michael Heizmann
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Depth sensor indoor surveillance; depth sensor networks; generative scene model; joint multi-view person detection; mean-field variational inference; multi-camera person detection
• Online Access: https://ieeexplore.ieee.org/document/8985332/

Privacy-preserving high-quality people detection is a vital computer vision task for various indoor scenarios, e.g. people counting, customer behavior analysis, ambient assisted living or smart homes. In this work a novel approach for people detection in multiple overlapping depth images is proposed. We present a probabilistic framework utilizing a generative scene model to jointly exploit the multi-view image evidence, allowing us to detect people from arbitrary viewpoints. Our approach makes use of mean-field variational inference to not only estimate the maximum a posteriori (MAP) state but to also approximate the posterior probability distribution of people present in the scene. Evaluation shows state-of-the-art results on a novel data set for indoor people detection and tracking in depth images from the top-view with high perspective distortions. Furthermore it can be demonstrated that our approach (compared to the the mono-view setup) successfully exploits the multi-view image evidence and robustly converges in only a few iterations.