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Optical Engineering

Online recognition of people recurrences with bag-of-features representation and automatic new-class labeling
Author(s): Kun Liu; Jie Yang
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Paper Abstract

In multicamera surveillance systems, tracking the same person across multiple cameras is an important technique. It is also desirable to recognize the individuals who have been previously observed with a single-camera monitor. This paper addresses this problem by recognizing the same individual in different tracks. The method that represents an object image using a bag of features has been commonly used in image retrieval and classification. In this paper, that approach is adapted for people image description, and support vector machines are employed for high classification performance. To get more reliable matches and support supervised learning in online operation, we propose a decision scheme to distinguish previously unseen individuals from recurrences so that the new classes can be automatically labeled. On this basis, an online recognition framework that applies incremental learning is also presented. We get promising results from the evaluation with more than 200 tracks of 70 different people.

Paper Details

Date Published: 1 January 2010
PDF: 10 pages
Opt. Eng. 49(1) 017203 doi: 10.1117/1.3281668
Published in: Optical Engineering Volume 49, Issue 1
Show Author Affiliations
Kun Liu, Shanghai Jiao Tong Univ. (China)
Jie Yang, Shanghai Jiao Tong Univ. (China)


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