Paper Info
Title
Multiple Person Re-Identification Using Part Based Spatio-Temporal Color Appearance Model
Abstract
In this paper, we address the problem of multiple person re-identification in the absence of calibration data or prior knowledge about the geospatial location of cameras. Multiple person re-identification is a open set matching problem with a dynamically evolving gallery and probe set. We present a part-based spatio-temporal model that learns a person's characteristic appearance as well as it's variations over time. The model is based on 2 distinct color features that capture the distribution of chromatic content and generates a signature of representative colors from a person's appearance. The model implicitly retains the meaningful variations while discarding the repetitive and noisy information and outliers. Re-identification is established based on solving a linear assignment problem in order to find a bijection that minimizes the total assignment cost between the gallery and probe pairs. Open and closed set re-identification is tested on 17 videos collected with 9 non-overlapping cameras spanning outdoor and indoor areas, with 25 subjects under observation. A false match rejection scheme based on the developed appearance model is also proposed(1).
Year
DOI
Venue
2011
10.1109/ICCVW.2011.6130457
2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCV WORKSHOPS)
Keywords
Field
DocType
active appearance model,feature extraction,linear assignment problem,calibration
Geospatial analysis,Computer vision,Bijection,Chromatic scale,Pattern recognition,Computer science,Outlier,Closed set,Active appearance model,Assignment problem,Artificial intelligence,Open set
Conference
Volume
Issue
Citations 
2011
1
17
PageRank 
References 
Authors
0.74
17
2
Name
Order
Citations
PageRank
Apurva Bedagkar-Gala11165.10
Shishir K Shah250140.08