Title :
Object tracking in infrared image sequence by Monte-Carlo method
Author :
Ma, Qianli ; Wang, Min
Abstract :
This paper presents a robust tracking algorithm for infrared objects in the image sequence, which is based on particle filer. Particle filter is a powerful tool for tracking especially in non-Gaussian condition, but the selection of samples is still a challenging problem. According to the frame-to-frame correlation, two basic assumptions are proposed. Borrowing the idea from Sequence Importance Sampling, Monte-Carlo method will be applied to solve the well-known shortcomings of Particle filter in this paper. Technologically, the proposed algorithm could also track multiple objects successfully. The experimental result has demonstrated its feasibility and validity.
Keywords :
Monte Carlo methods; image sampling; image sequences; object detection; optical correlation; optical tracking; particle filtering (numerical methods); Monte-Carlo method; frame-to-frame correlation; infrared image sequence; multiple object tracking; nonGaussian condition; object tracking; particle filter; sequence importance sampling; Bayesian methods; Histograms; Image sequences; Monte Carlo methods; Particle filters; Prediction algorithms; Signal processing algorithms; Monte-Carlo method; infrared object tracking; particle filter; wavelet denoise;
Conference_Titel :
Image and Signal Processing (CISP), 2010 3rd International Congress on
Conference_Location :
Yantai
Print_ISBN :
978-1-4244-6513-2
DOI :
10.1109/CISP.2010.5648033
