Accession Number:

ADA568129

Title:

Real-time Probabilistic Covariance Tracking with Efficient Model Update

Descriptive Note:

Conference paper

Corporate Author:

AIR FORCE RESEARCH LAB WRIGHT-PATTERSON AFB OH

Report Date:

2012-05-01

Pagination or Media Count:

16.0

Abstract:

The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties as well as their correlation are characterized. The similarity between two covariance descriptors is measured on Riemannian manifolds. Based on the same metric, but with a probabilistic framework, we propose a novel tracking approach on Riemannian manifolds with a novel incremental covariance tensor learning ICTL. To address the appearance variations, ICTL incrementally learns a low-dimensional covariance tensor representation and efficiently adapts online to appearance changes of the target with only O1 computational complexity, resulting in a real-time performance. The covariance-based representation and ICTL are then combined with the particle filter framework to allow better handling of background clutter as well as the temporary occlusions. We test the proposed probabilistic ICTL tracker on numerous benchmark sequences involving different types of challenges including occlusions and variations in illumination, scale, and pose. The proposed approach demonstrates excellent real-time performance, both qualitatively and quantitatively, in comparison with several previously proposed trackers.

Subject Categories:

  • Statistics and Probability
  • Target Direction, Range and Position Finding

Distribution Statement:

APPROVED FOR PUBLIC RELEASE