Adaptive visual tracking and recognition using particle filters

Authors:
Shaohua Zhou;R. Chellappa;B. Moghaddam
Affiliations:
Center for Autom. Res., Maryland Univ., College Park, MD, USA;Center for Autom. Res., Maryland Univ., College Park, MD, USA;Center for Autom. Res., Maryland Univ., College Park, MD, USA
Venue:
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Year:
2003

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Abstract

This paper presents an improved method for simultaneous tracking and recognition of human faces from video, where a time series model is used to resolve the uncertainties in tracking and recognition. The improvements mainly arise from three aspects: (i) modeling the inter-frame appearance changes within the video sequence using an adaptive appearance model and an adaptive-velocity motion model; (ii) modeling the appearance changes between the video frames and gallery images by constructing intra- and extra-personal spaces; and (iii) utilization of the fact that the gallery images are in frontal views. By embedding them in a particle filter, we are able to achieve a stabilized tracker and an accurate recognizer when confronted by pose and illumination variations.