Maximum-Likelihood Object Tracking from Multi-View Video by Combining Homography and Epipolar Constraints
Paper in proceeding, 2012

This paper addresses problem of object tracking in occlusion scenarios, where multiple uncalibrated cameras with overlapping fields of view are used. We propose a novel method where tracking is first done independently for each view and then tracking results are mapped between each pair of views to improve the tracking in individual views, under the assumptions that objects are not occluded in all views and move uprightly on a planar ground which may induce a homography relation between each pair of views. The tracking results are mapped by jointly exploiting the geometric constraints of homography, epipolar and vertical vanishing point. Main contributions of this paper include: (a) formulate a reference model of multi-view object appearance using region covariance for each view; (b) define a likelihood measure based on geodesics on a Riemannian manifold that is consistent with the destination view by mapping both the estimated positions and appearances of tracked object from other views; (c) locate object in each individual view based on maximum likelihood criterion from multi-view estimations of object position. Experiments have been conducted on videos from multiple uncalibrated cameras, where targets experience long-term partial or full occlusions. Comparison with two existing methods and performance evaluations are also made. Test results have shown effectiveness of the proposed method in terms of robustness against tracking drifts caused by occlusions.

epipolar geometry

multiple view geometry

visual object tracking

multiple cameras

planar homography

Author

Yixiao Yun

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Irene Yu-Hua Gu

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Hamid Aghajan

6th ACM/IEEE Int'l Conf on Distributed Smart Cameras (ICDSC 12), Oct 30 - Nov.2, 2012, Hong Kong

6 pages-
978-1-4503-1772-6 (ISBN)

Subject Categories

Language Technology (Computational Linguistics)

Computer and Information Science

Geometry

Information Science

Electrical Engineering, Electronic Engineering, Information Engineering

Signal Processing

Computer Science

Discrete Mathematics

Computer Vision and Robotics (Autonomous Systems)

Areas of Advance

Transport

Life Science Engineering (2010-2018)

ISBN

978-1-4503-1772-6

More information

Created

10/7/2017