Part-based spatio-temporal model for multi-person re-identification |
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Authors: | A. Bedagkar-Gala Shishir K. Shah |
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Affiliation: | 1. University of Nebraska – Omaha, 218 CPACS 6001 Dodge Street, Omaha, NE 68182, United States of America;2. Wisconsin Department of Corrections, 3099 East Washington Avenue, Madison, WI 53704, United States of America;3. California State University, 5151 State University Drive, Los Angeles, CA 90032, United States of America |
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Abstract: | In this paper we propose an adaptive part-based spatio-temporal model that characterizes person’s appearance using color and facial features. Face image selection based on low level cues is used to select usable face images to build a face model. Color features that capture the distribution of colors as well as the representative colors are used to build the color model. The model is built over a sequence of frames of an individual and hence captures the characteristic appearance as well as its variations over time. We also address the problem of multiple person re-identification in the absence of calibration data or prior knowledge about the camera layout. Multiple person re-identification is a open set matching problem with a dynamically evolving and open gallery set and an open probe set. Re-identification is posed as a rectangular assignment problem and is solved to find a bijection that minimizes the overall assignment cost. Open and closed set re-identification is tested on 30 videos collected with nine non-overlapping cameras spanning outdoor and indoor areas, with 40 subjects under observation. A false acceptance reduction scheme based on the developed model is also proposed. |
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