Multi-feature integration kernel particle filtering target tracking

CHU Hong-xia¹, ZHANG Ji-bin², WANG Ke-jun¹

1.College of automation,Harbin Engineering University,Harbin 150001,China;2.School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150090,China



Abstract:

In light of degradation of particle filtering and robust weakness in the utilization of single feature tracking,this paper presents a kernel particle filtering tracking method based on multi-feature integration.In this paper,a new weight upgrading method is given out during kernel particle filtering at first,and then robust tracking is realized by integrating color and texture features under the framework of kernel particle filtering.Space histogram and integral histogram is adopted to calculate color and texture features respectively.These two calculation methods effectively overcome their own defectiveness,and meanwhile,improve the real timing for particle filtering.This algorithm has also improved sampling effectiveness,resolved redundant calculation for particle filtering and degradation of particles.Finally,the experiment for target tracking is realized by using the method under complicated background and shelter.Experiment results show that the method can reliably and accurately track target and deal with target sheltering situation properly.

Key words:  kernel particle filtering  multi-feature integration  spatiograms  integral histogrom  tracking

DOI£º10.11916/j.issn.1005-9113.2011.06.006

Clc Number:TP391.41

Fund: