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Sequential Monte Carlo tracking by fusing multiple cues in video sequences

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Sequential Monte Carlo tracking by fusing multiple cues in video sequences. / Brasnett, P; Mihaylova, L.
In: Image and Vision Computing, Vol. 25, No. 8, 08.2007, p. 1217-1227.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Brasnett, P & Mihaylova, L 2007, 'Sequential Monte Carlo tracking by fusing multiple cues in video sequences', Image and Vision Computing, vol. 25, no. 8, pp. 1217-1227. https://doi.org/10.1016/j.imavis.2006.07.017

APA

Brasnett, P., & Mihaylova, L. (2007). Sequential Monte Carlo tracking by fusing multiple cues in video sequences. Image and Vision Computing, 25(8), 1217-1227. https://doi.org/10.1016/j.imavis.2006.07.017

Vancouver

Brasnett P, Mihaylova L. Sequential Monte Carlo tracking by fusing multiple cues in video sequences. Image and Vision Computing. 2007 Aug;25(8):1217-1227. doi: 10.1016/j.imavis.2006.07.017

Author

Brasnett, P ; Mihaylova, L. / Sequential Monte Carlo tracking by fusing multiple cues in video sequences. In: Image and Vision Computing. 2007 ; Vol. 25, No. 8. pp. 1217-1227.

Bibtex

@article{c77b7f9fbd1143adb2b8d45801136441,
title = "Sequential Monte Carlo tracking by fusing multiple cues in video sequences",
abstract = "This paper presents visual cues for object tracking in video sequences using particle filtering. A consistent histogram-based framework is developed for the analysis of colour, edge and texture cues. The visual models for the cues are learnt from the first frame and the tracking can be carried out using one or more of the cues. A method for online estimation of the noise parameters of the visual models is presented along with a method for adaptively weighting the cues when multiple models are used. A particle filter (PF) is designed for object tracking based on multiple cues with adaptive parameters. Its performance is investigated and evaluated with synthetic and natural sequences and compared with the mean-shift tracker. We show that tracking with multiple weighted cues provides more reliable performance than single cue tracking.",
keywords = "Particle filtering, Tracking in video sequences, Colour, Texture, Edges, Multiple cues, Bhattacharyya distance, DCS-publications-id, art-855, DCS-publications-credits, dsp, DCS-publications-personnel-id, 121",
author = "P Brasnett and L Mihaylova",
note = "The final, definitive version of this article has been published in the Journal, Image and Vision Computing, 25 (8), 2007, {\textcopyright} ELSEVIER.",
year = "2007",
month = aug,
doi = "10.1016/j.imavis.2006.07.017",
language = "English",
volume = "25",
pages = "1217--1227",
journal = "Image and Vision Computing",
issn = "0262-8856",
publisher = "Elsevier Limited",
number = "8",

}

RIS

TY - JOUR

T1 - Sequential Monte Carlo tracking by fusing multiple cues in video sequences

AU - Brasnett, P

AU - Mihaylova, L

N1 - The final, definitive version of this article has been published in the Journal, Image and Vision Computing, 25 (8), 2007, © ELSEVIER.

PY - 2007/8

Y1 - 2007/8

N2 - This paper presents visual cues for object tracking in video sequences using particle filtering. A consistent histogram-based framework is developed for the analysis of colour, edge and texture cues. The visual models for the cues are learnt from the first frame and the tracking can be carried out using one or more of the cues. A method for online estimation of the noise parameters of the visual models is presented along with a method for adaptively weighting the cues when multiple models are used. A particle filter (PF) is designed for object tracking based on multiple cues with adaptive parameters. Its performance is investigated and evaluated with synthetic and natural sequences and compared with the mean-shift tracker. We show that tracking with multiple weighted cues provides more reliable performance than single cue tracking.

AB - This paper presents visual cues for object tracking in video sequences using particle filtering. A consistent histogram-based framework is developed for the analysis of colour, edge and texture cues. The visual models for the cues are learnt from the first frame and the tracking can be carried out using one or more of the cues. A method for online estimation of the noise parameters of the visual models is presented along with a method for adaptively weighting the cues when multiple models are used. A particle filter (PF) is designed for object tracking based on multiple cues with adaptive parameters. Its performance is investigated and evaluated with synthetic and natural sequences and compared with the mean-shift tracker. We show that tracking with multiple weighted cues provides more reliable performance than single cue tracking.

KW - Particle filtering

KW - Tracking in video sequences

KW - Colour

KW - Texture

KW - Edges

KW - Multiple cues

KW - Bhattacharyya distance

KW - DCS-publications-id

KW - art-855

KW - DCS-publications-credits

KW - dsp

KW - DCS-publications-personnel-id

KW - 121

U2 - 10.1016/j.imavis.2006.07.017

DO - 10.1016/j.imavis.2006.07.017

M3 - Journal article

VL - 25

SP - 1217

EP - 1227

JO - Image and Vision Computing

JF - Image and Vision Computing

SN - 0262-8856

IS - 8

ER -