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Particle Filtering with Multiple Cues for Object Tracking in Video Sequences

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Particle Filtering with Multiple Cues for Object Tracking in Video Sequences. / Brasnett, P.; Mihaylova, L.; Canagarajah, N. et al.
SPIE Proceedings. Vol. 5685 SPIE, 2005. p. 430-441.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Brasnett, P, Mihaylova, L, Canagarajah, N & Bull, D 2005, Particle Filtering with Multiple Cues for Object Tracking in Video Sequences. in SPIE Proceedings. vol. 5685, SPIE, pp. 430-441, IS &T/SPIE's 17th Annual Symposium on Electronic Imaging, Science and Technology, San Jose California, USA, 16/01/05. https://doi.org/10.1117/12.585882

APA

Brasnett, P., Mihaylova, L., Canagarajah, N., & Bull, D. (2005). Particle Filtering with Multiple Cues for Object Tracking in Video Sequences. In SPIE Proceedings (Vol. 5685, pp. 430-441). SPIE. https://doi.org/10.1117/12.585882

Vancouver

Brasnett P, Mihaylova L, Canagarajah N, Bull D. Particle Filtering with Multiple Cues for Object Tracking in Video Sequences. In SPIE Proceedings. Vol. 5685. SPIE. 2005. p. 430-441 doi: 10.1117/12.585882

Author

Brasnett, P. ; Mihaylova, L. ; Canagarajah, N. et al. / Particle Filtering with Multiple Cues for Object Tracking in Video Sequences. SPIE Proceedings. Vol. 5685 SPIE, 2005. pp. 430-441

Bibtex

@inproceedings{92532e94d8374b988c77cd551c32ae1d,
title = "Particle Filtering with Multiple Cues for Object Tracking in Video Sequences",
abstract = "In this paper we investigate object tracking in video sequences by using the potential of particle filtering to process features from video frames. A particle filter (PF) and a Gaussian sum particle filter (GSPF) are developed based upon multiple information cues, namely colour and texture, which are described with highly nonlinear models. The algorithms rely on likelihood factorisation as a product of the likelihoods of the cues. We demonstrate the advantages of tracking with multiple independent complementary cues compared to tracking with individual cues. The advantages are increased robustness and improved accuracy. The performance of the two filters is investigated and validated over both synthetic and natural video sequences.",
keywords = "particle filtering, Bayesian methods, tracking in video sequences, colour, texture, Gaussian sum particle filtering, DCS-publications-id, inproc-429, DCS-publications-credits, dsp, DCS-publications-personnel-id, 121",
author = "P. Brasnett and L. Mihaylova and N. Canagarajah and D. Bull",
note = "Publisher: SPIE; IS &T/SPIE's 17th Annual Symposium on Electronic Imaging, Science and Technology ; Conference date: 16-01-2005 Through 20-01-2005",
year = "2005",
month = jan,
day = "16",
doi = "10.1117/12.585882",
language = "English",
volume = "5685",
pages = "430--441",
booktitle = "SPIE Proceedings",
publisher = "SPIE",

}

RIS

TY - GEN

T1 - Particle Filtering with Multiple Cues for Object Tracking in Video Sequences

AU - Brasnett, P.

AU - Mihaylova, L.

AU - Canagarajah, N.

AU - Bull, D.

N1 - Publisher: SPIE

PY - 2005/1/16

Y1 - 2005/1/16

N2 - In this paper we investigate object tracking in video sequences by using the potential of particle filtering to process features from video frames. A particle filter (PF) and a Gaussian sum particle filter (GSPF) are developed based upon multiple information cues, namely colour and texture, which are described with highly nonlinear models. The algorithms rely on likelihood factorisation as a product of the likelihoods of the cues. We demonstrate the advantages of tracking with multiple independent complementary cues compared to tracking with individual cues. The advantages are increased robustness and improved accuracy. The performance of the two filters is investigated and validated over both synthetic and natural video sequences.

AB - In this paper we investigate object tracking in video sequences by using the potential of particle filtering to process features from video frames. A particle filter (PF) and a Gaussian sum particle filter (GSPF) are developed based upon multiple information cues, namely colour and texture, which are described with highly nonlinear models. The algorithms rely on likelihood factorisation as a product of the likelihoods of the cues. We demonstrate the advantages of tracking with multiple independent complementary cues compared to tracking with individual cues. The advantages are increased robustness and improved accuracy. The performance of the two filters is investigated and validated over both synthetic and natural video sequences.

KW - particle filtering

KW - Bayesian methods

KW - tracking in video sequences

KW - colour

KW - texture

KW - Gaussian sum particle filtering

KW - DCS-publications-id

KW - inproc-429

KW - DCS-publications-credits

KW - dsp

KW - DCS-publications-personnel-id

KW - 121

U2 - 10.1117/12.585882

DO - 10.1117/12.585882

M3 - Conference contribution/Paper

VL - 5685

SP - 430

EP - 441

BT - SPIE Proceedings

PB - SPIE

T2 - IS &T/SPIE's 17th Annual Symposium on Electronic Imaging, Science and Technology

Y2 - 16 January 2005 through 20 January 2005

ER -