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Structural similarity-based object tracking in multimodality surveillance videos

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Structural similarity-based object tracking in multimodality surveillance videos. / Łoza, Artur; Mihaylova, Lyudmila; Bull, David et al.
In: Machine Vision and Applications, Vol. 20, No. 2, 02.2009, p. 71-83.

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

Harvard

Łoza, A, Mihaylova, L, Bull, D & Canagarajah, N 2009, 'Structural similarity-based object tracking in multimodality surveillance videos', Machine Vision and Applications, vol. 20, no. 2, pp. 71-83. https://doi.org/10.1007/s00138-007-0107-x

APA

Łoza, A., Mihaylova, L., Bull, D., & Canagarajah, N. (2009). Structural similarity-based object tracking in multimodality surveillance videos. Machine Vision and Applications, 20(2), 71-83. https://doi.org/10.1007/s00138-007-0107-x

Vancouver

Łoza A, Mihaylova L, Bull D, Canagarajah N. Structural similarity-based object tracking in multimodality surveillance videos. Machine Vision and Applications. 2009 Feb;20(2):71-83. doi: 10.1007/s00138-007-0107-x

Author

Łoza, Artur ; Mihaylova, Lyudmila ; Bull, David et al. / Structural similarity-based object tracking in multimodality surveillance videos. In: Machine Vision and Applications. 2009 ; Vol. 20, No. 2. pp. 71-83.

Bibtex

@article{0066a2979e5a4ad6b915b4bc6c1d20a6,
title = "Structural similarity-based object tracking in multimodality surveillance videos",
abstract = "This paper addresses the problem of object tracking in video sequences for surveillance applications by using a recently proposed structural similarity-based image distance measure. Multimodality surveillance videos pose specific challenges to tracking algorithms, due to, for example, low or variable light conditions and the presence of spurious or camouflaged objects. These factors often cause undesired luminance and contrast variations in videos produced by infrared sensors (due to varying thermal conditions) and visible sensors (e.g., the object entering shadowy areas). Commonly used colour and edge histogram-based trackers often fail in such conditions. In contrast, the structural similarity measure reflects the distance between two video frames by jointly comparing their luminance, contrast and spatial characteristics and is sensitive to relative rather than absolute changes in the video frame. In this work, we show that the performance of a particle filter tracker is improved significantly when the structural similarity-based distance is applied instead of the conventional Bhattacharyya histogram-based distance. Extensive evaluation of the proposed algorithm is presented together with comparisons with colour, edge and mean-shift trackers using real-world surveillance video sequences from multimodal (infrared and visible) cameras.",
keywords = "Structural similarity measure · Object tracking · Video sequences · Particle filtering · Colour and edge cues · Multimodal data, DCS-publications-id, art-903, DCS-publications-credits, dsp, DCS-publications-personnel-id, 121",
author = "Artur {\L}oza and Lyudmila Mihaylova and David Bull and Nishan Canagarajah",
year = "2009",
month = feb,
doi = "10.1007/s00138-007-0107-x",
language = "English",
volume = "20",
pages = "71--83",
journal = "Machine Vision and Applications",
issn = "1432-1769",
publisher = "Springer Verlag",
number = "2",

}

RIS

TY - JOUR

T1 - Structural similarity-based object tracking in multimodality surveillance videos

AU - Łoza, Artur

AU - Mihaylova, Lyudmila

AU - Bull, David

AU - Canagarajah, Nishan

PY - 2009/2

Y1 - 2009/2

N2 - This paper addresses the problem of object tracking in video sequences for surveillance applications by using a recently proposed structural similarity-based image distance measure. Multimodality surveillance videos pose specific challenges to tracking algorithms, due to, for example, low or variable light conditions and the presence of spurious or camouflaged objects. These factors often cause undesired luminance and contrast variations in videos produced by infrared sensors (due to varying thermal conditions) and visible sensors (e.g., the object entering shadowy areas). Commonly used colour and edge histogram-based trackers often fail in such conditions. In contrast, the structural similarity measure reflects the distance between two video frames by jointly comparing their luminance, contrast and spatial characteristics and is sensitive to relative rather than absolute changes in the video frame. In this work, we show that the performance of a particle filter tracker is improved significantly when the structural similarity-based distance is applied instead of the conventional Bhattacharyya histogram-based distance. Extensive evaluation of the proposed algorithm is presented together with comparisons with colour, edge and mean-shift trackers using real-world surveillance video sequences from multimodal (infrared and visible) cameras.

AB - This paper addresses the problem of object tracking in video sequences for surveillance applications by using a recently proposed structural similarity-based image distance measure. Multimodality surveillance videos pose specific challenges to tracking algorithms, due to, for example, low or variable light conditions and the presence of spurious or camouflaged objects. These factors often cause undesired luminance and contrast variations in videos produced by infrared sensors (due to varying thermal conditions) and visible sensors (e.g., the object entering shadowy areas). Commonly used colour and edge histogram-based trackers often fail in such conditions. In contrast, the structural similarity measure reflects the distance between two video frames by jointly comparing their luminance, contrast and spatial characteristics and is sensitive to relative rather than absolute changes in the video frame. In this work, we show that the performance of a particle filter tracker is improved significantly when the structural similarity-based distance is applied instead of the conventional Bhattacharyya histogram-based distance. Extensive evaluation of the proposed algorithm is presented together with comparisons with colour, edge and mean-shift trackers using real-world surveillance video sequences from multimodal (infrared and visible) cameras.

KW - Structural similarity measure · Object tracking · Video sequences · Particle filtering · Colour and edge cues · Multimodal data

KW - DCS-publications-id

KW - art-903

KW - DCS-publications-credits

KW - dsp

KW - DCS-publications-personnel-id

KW - 121

U2 - 10.1007/s00138-007-0107-x

DO - 10.1007/s00138-007-0107-x

M3 - Journal article

VL - 20

SP - 71

EP - 83

JO - Machine Vision and Applications

JF - Machine Vision and Applications

SN - 1432-1769

IS - 2

ER -