Markov random field modeled level sets method for object tracking with moving cameras

Authors:
Xue Zhou;Weiming Hu;Ying Chen;Wei Hu
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Beijing, China
Venue:
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Year:
2007

Citing 11
Cited 2

Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations

Journal of Computational Physics
A fast level set method for propagating interfaces

Journal of Computational Physics
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Contours

International Journal of Computer Vision
Pfinder: Real-Time Tracking of the Human Body

IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects

IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities

IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Multi-Channel Image with Markov Random Field Based Active Contour Model

Journal of VLSI Signal Processing Systems
Real-Time Tracking Using Level Sets

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Dynamical Statistical Shape Priors for Level Set-Based Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active contours without edges

IEEE Transactions on Image Processing

Robust contour tracking based on a coupling between geodesic active contours and conditional random fields

Journal of Visual Communication and Image Representation
Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

Object tracking using active contours has attracted increasing interest in recent years due to acquisition of effective shape descriptions. In this paper, an object tracking method based on level sets using moving cameras is proposed. We develop an automatic contour initialization method based on optical flow detection. A Markov Random Field (MRF)-like model measuring the correlations between neighboring pixels is added to improve the general region-based level sets speed model. The experimental results on several real video sequences show that our method successfully tracks objects despite object scale changes, motion blur, background disturbance, and gets smoother and more accurate results than the current region-based method.