Multi-modal RGB–Depth–Thermal Human Body Segmentation

• Published in: International journal of computer vision Vol. 118; no. 2; pp. 217 - 239
• Main Authors: Palmero, Cristina, Clapés, Albert, Bahnsen, Chris, Møgelmose, Andreas, Moeslund, Thomas B., Escalera, Sergio
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2016; Springer; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Calibration; Computer Imaging; Computer Science; Computer vision; Data mining; Datasets; Feature extraction; Human acts; Human behavior; Human body; Human subjects; Image Processing and Computer Vision; Image processing systems; Learning; Machine learning; Mathematical analysis; Pattern Recognition; Pattern Recognition and Graphics; Probabilistic methods; Probability theory; Registration; Segmentation; State of the art; Studies; Support vector machines; Vision; Vision systems; RGB; Depth; Human body segmentation; Thermal

This work addresses the problem of human body segmentation from multi-modal visual cues as a first stage of automatic human behavior analysis. We propose a novel RGB–depth–thermal dataset along with a multi-modal segmentation baseline. The several modalities are registered using a calibration device...