Information fusion from multiple cameras for gait-based re-identification and recognition

• Published in: IET image processing Vol. 9; no. 11; pp. 969 - 976
• Main Authors: Chattopadhyay, Pratik, Sural, Shamik, Mukherjee, Jayanta
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.11.2015
• Subjects: Automation; Cameras; Feature extraction; Gait; Gait recognition; gait recognition approach; gait‐based re‐identification; image recognition; image sensors; information fusion; Kinect software development kit; medical image processing; multiple cameras; multiple kinect RGB‐D cameras; sensor fusion; Software development; Special Issue: Machine Learning in Image Processing; Streams; Video data; image sensors; information fusion; multiple cameras; gait-based re-identification; multiple kinect RGB-D cameras; feature extraction; sensor fusion; gait recognition approach; image recognition; medical image processing; Kinect software development kit
• ISSN: 1751-9659; 1751-9667
• DOI: 10.1049/iet-ipr.2014.0773

In this study, the authors present a fully automated frontal (i.e. employing front and back views only) gait recognition approach using the depth information captured by multiple Kinect RGB-D cameras. Limited depth sensing range restricts each of these Kinects to record only a part of a complete gait cycle of a walking subject. Hence, information from more than one Kinect is fused together to examine which features of a gait cycle can be conveniently extracted from the sequences captured independently by these cameras. To achieve this, it is imperative that the same subject be re-identified as he moves from the field of view of one camera to another. The authors use a set of soft-biometric features computed from the skeleton stream provided by Kinect software development kit) for doing automatic re-identification. To enable such information fusion and also to handle missing components even after re-identification, features are extracted at the granularity of small fractions of a gait cycle. Experiments carried out on a data set with gait videos captured by Kinects respectively from the back and front views show promising results.