Optimal recursive clustering of likelihood functions for multiple object tracking

Authors:
Séverine Dubuisson;Jonathan Fabrizio
Affiliations:
Laboratoire d'Informatique de Paris 6 (LIP6/UPMC), 104 Avenue du Président Kennedy, 75016 Paris, France;Laboratoire d'Informatique de Paris 6 (LIP6/UPMC), 104 Avenue du Président Kennedy, 75016 Paris, France
Venue:
Pattern Recognition Letters
Year:
2009

Citing 14
Cited 1

Algorithms for clustering data

Algorithms for clustering data
Image Representation Using 2D Gabor Wavelets

IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation

International Journal of Computer Vision
Efficient Region Tracking With Parametric Models of Geometry and Illumination

IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion tracking of human mouth by generalized deformable models

Pattern Recognition Letters
3D game engine design: a practical approach to real-time computer graphics

3D game engine design: a practical approach to real-time computer graphics
Robust motion estimation using spatial Gabor-like filters

Signal Processing
Kernel-Based Object Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active blobs: region-based, deformable appearance models

Computer Vision and Image Understanding - Special issue on nonrigid image registration
Non-linear matched filtering for object detection and tracking

Pattern Recognition Letters
A multi-Kalman filtering approach for video tracking of human-delineated objects in cluttered environments

Computer Vision and Image Understanding
Fast Multiple Object Tracking via a Hierarchical Particle Filter

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A statistics-based approach to control the quality of subclusters in incremental gravitational clustering

Pattern Recognition
Sequential Monte Carlo methods for multiple target tracking anddata fusion

IEEE Transactions on Signal Processing

A scalable privacy-preserving recommendation scheme via bisecting k-means clustering

Information Processing and Management: an International Journal

Quantified Score

Hi-index	0.10

Visualization

Abstract

In this paper, we propose a method to track multiple deformable objects in sequences (with a static camera) in and beyond the visible spectrum by combining Gabor filtering and clustering. The idea is to sample moving areas between two frames by randomly positioning samples over high magnitude area of a motion likelihood function. These points are then clustered to obtain one class for each moving object. The novelty in our method is in using cluster information from the previous frame to classify new samples in the current frame: we call that a recursive clustering. This makes our method robust to occlusions, objects entering and leaving the field of view, objects stopping and starting, and moving objects getting really close to each other.