Adaptive detection of moving objects using multiscale techniques
- 23 December 2002
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1, 525-528
- https://doi.org/10.1109/icip.1996.559549
Abstract
In this paper we address an important issue in motion analysis: the detection of moving objects. A statistical approach is adopted in order to formulate the problem. The inter-frame difference is modeled by a mixture of Laplacian distributions, and a Gibbs random field is used for describing the label set. A new method to determine the regularization parameter is proposed, based on a voting technique. Then two different multiscale algorithms are evaluated, and the labeling problem is solved using either ICM (Iterated Conditional Modes) or HCF (Highest Confidence First) algorithms. Experimental results are provided using synthetic and real video sequences.Keywords
This publication has 11 references indexed in Scilit:
- Detection and location of moving objects using deterministic relaxation algorithmsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1996
- Robust Multiresolution Estimation of Parametric Motion ModelsJournal of Visual Communication and Image Representation, 1995
- Bayesian algorithms for adaptive change detection in image sequences using Markov random fieldsSignal Processing: Image Communication, 1995
- Change detection and texture analysis for image sequence codingSignal Processing: Image Communication, 1994
- Multiscale Minimization of Global Energy Functions in Some Visual Recovery ProblemsCVGIP: Image Understanding, 1994
- On the choice of the regularization parameter: the case of binary images in the Bayesian restoration frameworkPublished by Institute of Mathematical Statistics ,1991
- The theory and practice of Bayesian image labelingInternational Journal of Computer Vision, 1990
- Ill-posed problems in early visionProceedings of the IEEE, 1988
- Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random FieldsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1987
- A Fast and Efficient Cross-Validation Method for Smoothing Parameter Choice in Spline RegressionJournal of the American Statistical Association, 1984