Multiple Object Tracking with Kernel Particle Filter
- 27 July 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
A new particle filter, kernel particle filter (KPF), is proposed for visual tracking for multiple objects in image sequences. The KPF invokes kernels to form a continuous estimate of the posterior density function and allocates particles based on the gradient derived from the kernel density estimate. A data association technique is also proposed to resolve the motion correspondence ambiguities that arise when multiple objects are present. The data association technique introduces minimal amount of computation by making use of the intermediate results obtained in particle allocation. We show that KPF performs robust multiple object tracking with improved sampling efficiency.Keywords
This publication has 11 references indexed in Scilit:
- Kernel particle filter for visual trackingIEEE Signal Processing Letters, 2005
- Kernel-based object trackingIEEE Transactions on Pattern Analysis and Machine Intelligence, 2003
- Adaptive background mixture models for real-time trackingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Maintaining multimodality through mixture trackingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Sequential Monte Carlo methods for multiple target tracking and data fusionIEEE Transactions on Signal Processing, 2002
- Probabilistic data association methods for tracking complex visual objectsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2001
- Improving Regularised Particle FiltersPublished by Springer Nature ,2001
- A probabilistic exclusion principle for tracking multiple objectsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1999
- Mean shift, mode seeking, and clusteringIEEE Transactions on Pattern Analysis and Machine Intelligence, 1995
- Density Estimation for Statistics and Data AnalysisPublished by Springer Nature ,1400