Robust autocalibration for a surveillance camera network

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

We propose a novel approach for multi-camera autocalibration by observing multiview surveillance video of pedestrians walking through the scene. Unlike existing methods, we do NOT require tracking or explicit correspondences of the same person across time/views. Instead, we take noisy foreground blobs as the only input and rely on a joint optimization framework with robust statistics to achieve accurate calibration under challenging scenarios. First, each individual camera is roughly calibrated into its local World Coordinate System (lWCS) based on analysis of relative 3D pedestrian height distribution. Then, all lWCSs are iteratively registered with respect to a shared global World Coordinate System (gWCS) by incorporating robust matching with a partial Direct Linear Transform (pDLT). As demonstrated by extensive evaluation, our algorithm achieves satisfactory results in various camera settings with up to moderate crowd densities with a large proportion of foreground outliers.

Original languageEnglish (US)
Title of host publication2013 IEEE Workshop on Applications of Computer Vision, WACV 2013
Pages433-440
Number of pages8
DOIs
StatePublished - 2013
Event2013 IEEE Workshop on Applications of Computer Vision, WACV 2013 - Clearwater Beach, FL, United States
Duration: Jan 15 2013Jan 17 2013

Publication series

NameProceedings of IEEE Workshop on Applications of Computer Vision
ISSN (Print)2158-3978
ISSN (Electronic)2158-3986

Other

Other2013 IEEE Workshop on Applications of Computer Vision, WACV 2013
CountryUnited States
CityClearwater Beach, FL
Period1/15/131/17/13

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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