Non-linear matched filtering for object detection and tracking

Authors:
J.-C. Noyer;P. Lanvin;M. Benjelloun
Affiliations:
Laboratoire d'Analyse des Systèmes du Littoral (EA 2600), Université du Littoral Côte d'Opale, B.P. 699, 50 Rue F. Buisson, 62228 Calais Cedex, France;Laboratoire d'Analyse des Systèmes du Littoral (EA 2600), Université du Littoral Côte d'Opale, B.P. 699, 50 Rue F. Buisson, 62228 Calais Cedex, France;Laboratoire d'Analyse des Systèmes du Littoral (EA 2600), Université du Littoral Côte d'Opale, B.P. 699, 50 Rue F. Buisson, 62228 Calais Cedex, France
Venue:
Pattern Recognition Letters
Year:
2004

Citing 10
Cited 3

Registration of Translated and Rotated Images Using Finite Fourier Transforms

IEEE Transactions on Pattern Analysis and Machine Intelligence
On the estimation of optical flow: relations between different approaches and some new results

Artificial Intelligence
Tracking line segments

Image and Vision Computing - Special issue on the first ECCV 1990
Determining motion from 3D line segment matches: a comparative study

Image and Vision Computing
Three-dimensional computer vision: a geometric viewpoint

Three-dimensional computer vision: a geometric viewpoint
Affine analysis of image sequences

Affine analysis of image sequences
CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
Contour Tracking by Stochastic Propagation of Conditional Density

ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
3D particle tracking using an active vision

Pattern Recognition Letters
Extension of phase correlation to subpixel registration

IEEE Transactions on Image Processing

Optimal recursive clustering of likelihood functions for multiple object tracking

Pattern Recognition Letters
Correlation-based particle filter for 3D object tracking

Integrated Computer-Aided Engineering
Maneuvering targets state prediction based on robust H∞ filtering in opto-electronic tracking system

Signal Processing

Quantified Score

Hi-index	0.10

Visualization

Abstract

In this paper, we present a method for detecting and tracking rigid moving objects in a monocular image sequence. The originality of this method lies in a state modelling of this estimation problem which is solved in an unified way. This hybrid estimation problem leads to non-linear state equations that are solved by the particle method. A particle filter is set for each shape model (modes). It estimates the motion and position parameters and tracks the object in the sequence. The algorithm also computes at each time the probability of all modes. This method is then applied to synthetic and real image sequences in order to evaluate the estimation accuracies and the robustness of the tracking procedure.