Error analysis of modeling in a low-resolution color-based visual tracker

Author

Lu, Xin ; Nishiyama, Kiyoshi

Author_Institution

Fac. of Eng., Iwate Univ., Morioka, Japan

fYear

2009

fDate

7-9 Jan. 2009

Firstpage

33

Lastpage

36

Abstract

If the Kalman filter is effective to enhance the CAMSHIFT tracker in the low-resolution image sequence, the state-space model (SSM) that the Kalman filter is based on needs satisfying three stochastic conditions: (1) the observation noise is relatively small; (2) the state noise and observation noise are independent to each other; (3) the distribution of the observation noise is near to Gaussian distribution. In this paper, given the maximum likelihood (ML) estimator to exactly incorporate the quantization information of the tracking results into the observation matrix and observation noise matrix of the SSM, these conditions can be acquired.

Keywords

Gaussian distribution; Kalman filters; computer vision; error analysis; image colour analysis; image resolution; image sequences; maximum likelihood estimation; quantisation (signal); CAMSHIFT tracker; Gaussian distribution; Kalman filter; continuously adaptive mean shift; error analysis; low-resolution color-based visual tracker; low-resolution image sequence; maximum likelihood estimator; observation matrix; observation noise; quantization information; state noise; state-space model; stochastic condition; Colored noise; Error analysis; Gaussian distribution; Gaussian noise; Image sequences; Mathematical model; Maximum likelihood estimation; Quantization; Signal processing; Vehicle safety;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Signal Processing and Communication Systems, 2009. ISPACS 2009. International Symposium on

Conference_Location

Kanazawa

Print_ISBN

978-1-4244-5015-2

Electronic_ISBN

978-1-4244-5016-9

Type

conf

DOI

10.1109/ISPACS.2009.5383907

Filename

5383907