Spatio-temporal target-measure association using an adaptive geometrical approach

Authors:
Abir El Abed;Séverine Dubuisson;Dominique Béréziat
Affiliations:
Laboratoire d'Informatique de Paris 6, Université Pierre et Marie Curie, 4 place Jussieu, 75005 Paris, France;Laboratoire d'Informatique de Paris 6, Université Pierre et Marie Curie, 4 place Jussieu, 75005 Paris, France;Laboratoire d'Informatique de Paris 6, Université Pierre et Marie Curie, 4 place Jussieu, 75005 Paris, France
Venue:
Pattern Recognition Letters
Year:
2012

Citing 11
Cited 0

Tracking and data association

Tracking and data association
Decision estimation and classification: an introduction to pattern recognition and related topics

Decision estimation and classification: an introduction to pattern recognition and related topics
A review of statistical data association for motion correspondence

International Journal of Computer Vision
Learning and Classification of Complex Dynamics

IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Data Association Methods for Tracking Complex Visual Objects

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Contours: The Application of Techniques from Graphics,Vision,Control Theory and Statistics to Visual Tracking of Shapes in Motion

Active Contours: The Application of Techniques from Graphics,Vision,Control Theory and Statistics to Visual Tracking of Shapes in Motion
A study of a target tracking algorithm using global nearest neighbor approach

CompSysTech '03 Proceedings of the 4th international conference conference on Computer systems and technologies: e-Learning
A probabilistic nearest neighbor filter algorithm for tracking in a clutter environment

Signal Processing
Object tracking: A survey

ACM Computing Surveys (CSUR)
Comparison of statistical and shape-based approaches for non-rigid motion tracking with missing data using a particle filter

ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Derivation and evaluation of improved tracking filter for use in dense multitarget environments

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.10

Visualization

Abstract

Data association is of crucial importance to improve target tracking performance in many complex visual environments (non-linear dynamics, occlusions, etc). Usually, association effectiveness is based on prior information and observation category. However, association becomes difficult if targets are similar. Problems also arise in cases of missing data, complex motions or deformations over time. To remedy, we propose a new method for data association, that uses the evolution of the dynamic model of targets. The main idea is to measure an adaptive geometric accuracy between possible trajectories of targets, by only using positions as information, that constitutes its main advantage.