BibTeX
@MISC{_multi-cameracalibration,,
author = {},
title = {MULTI-CAMERA CALIBRATION, OBJECT TRACKING AND QUERY GENERATION},
year = {}
}
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Abstract
An automatic object tracking and video summarization method for multi-camera systems with a large number of non-overlapping field-of-view cameras is explained. In this framework, video sequences are stored for each object as opposed to storing a sequence for each camera. Object-based representation enables annotation of video segments, and extraction of content semantics for further analysis. We also present a novel solution to the inter-camera color cali-bration problem. The transitive model function enables ef-fective compensation for lighting changes and radiometric distortions for large-scale systems. After.initial calibration, objects are tracked at each camera by background subtrac-tion and mean-shift analysis. The correspondence of ob-jects between different cameras is established by using a BayeSian Belief Network. This framework empowers the user to get a concise response to queries such as “which lo-cations did an object visit on Monday and what did it do there?” 1.
Keyphrases
query generation multi-camera calibration object tracking video sequence transitive model function non-overlapping field-of-view camera multi-camera system initial calibration automatic object tracking large number different camera novel solution bayesian belief network video segment object-based representation mean-shift analysis large-scale system content semantics concise response object visit inter-camera color cali-bration problem radiometric distortion video summarization method ef-fective compensation
