An adaptive Bayesian technique for tracking multiple objects
Date
2007
Authors
Kumar, P.
Brooks, M.
Van Den Hengel, A.
Editors
Ashish Ghosh,
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Pattern Recognition and Machine Intelligence / David Hutchison ... [et al.] (eds.):657-665
Statement of Responsibility
Pankaj Kumar, Michael J. Brooks and Anton van den Hengel
Conference Name
International Conference on Pattern Recognition and Machine Intelligence [PReMI] (2nd : 2007 : Kolkata, India)
Abstract
Robust tracking of objects in video is a key challenge in computer vision with applications in automated surveillance, video indexing, human-computer-interaction, gesture recognition, traffic monitoring, etc. Many algorithms have been developed for tracking an object in controlled environments. However, they are susceptible to failure when the challenge is to track multiple objects that undergo appearance change to due to factors such as variation in illumination and object pose. In this paper we present a tracker based on Bayesian estimation, which is relatively robust to object appearance change, and can track multiple targets simultaneously in real time. The object model for computing the likelihood function is incrementally updated and uses background-foreground segmentation information to ameliorate the problem of drift associated with object model update schemes. We demonstrate the efficacy of the proposed method by tracking objects in image sequences from the CAVIAR dataset.
School/Discipline
Dissertation Note
Provenance
Description
The original publication can be found at www.springerlink.com