Improved Method Based on MPF for Multi-target Tracking

• Published in: AASRI procedia Vol. 3; pp. 177 - 183
• Main Authors: Lin, Qing, Yang, Yaping, Yin, Ying, Wang, Shitong, Liao, Dingan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2012
• Subjects: Gaussian Particle Filtering; GM-MPF; Mixture Particle filter; Particle degradation; Particle filter; Mixture Particle filter; Gaussian Particle Filtering; GM-MPF; Particle filter; Particle degradation
• ISSN: 2212-6716; 2212-6724; 2212-6724
• DOI: 10.1016/j.aasri.2012.11.030

Particle filter algorithm is poor at consistently maintaining the multi-modality problem and remains particle degeneracy phenomenon. This shortcoming can be addressed through using Mixture Particle Filter (MPF) to model the target distribution as a non-parametric mixture model. According to computational complexity of the particle filter algorithm, Gaussian Particle Filter (GPF) is used. The particle set is obtained by sampling of Gaussian density function instead of re-sampling to improve the computing speed of particle filter algorithm. In this paper, combined with the advantages of GPF and MPF, GM-MPF algorithm is proposed to improve the real-time of MPF. The experimental results show that GM-MPF algorithm can effectively solve the problem of multi-modality, particle degradation and achieve real-time multi-target tracking.