Viewpoint Invariant Human Re-Identification in Camera Networks Using Pose Priors and Subject-Discriminative Features

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 37; no. 5; pp. 1095 - 1108
• Main Authors: Ziyan Wu, Yang Li, Radke, Richard J.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Camera Networks; Cameras; Feature extraction; Histograms; Human Re-Identification; Image color analysis; Measurement; Performance evaluation; Strips; Surveillance; Viewpoint invariance; viewpoint invariance; camera networks; Human re-identification
• DOI: 10.1109/TPAMI.2014.2360373
• ISSN: 0162-8828

Human re-identification across cameras with non-overlapping fields of view is one of the most important and difficult problems in video surveillance and analysis. However, current algorithms are likely to fail in real-world scenarios for several reasons. For example, surveillance cameras are typically mounted high above the ground plane, causing serious perspective changes. Also, most algorithms approach matching across images using the same descriptors, regardless of camera viewpoint or human pose. Here, we introduce a re-identification algorithm that addresses both problems. We build a model for human appearance as a function of pose, using training data gathered from a calibrated camera. We then apply this "pose prior" in online re-identification to make matching and identification more robust to viewpoint. We further integrate person-specific features learned over the course of tracking to improve the algorithm's performance. We evaluate the performance of the proposed algorithm and compare it to several state-of-the-art algorithms, demonstrating superior performance on standard benchmarking datasets as well as a challenging new airport surveillance scenario.