Abstract
Single object tracker is a very important building block of an autonomous patient monitoring system. A good tracker will enable the system to determine the state of the patient accurately. A precise tracker will provide an accurate size of the tracked bounding box for patient analysis. However, illumination change always disrupts the tracker performance for both indoor and outdoor environments. The aim of this paper is to develop a tracker that is robust to illumination variation, especially towards uniform and sudden change. CAMSHIFT tracker has been selected as the base tracker where white patch retinex approach has been fused to. A histogram based decision is used for selecting the clipping pixel. While RGB space is used for retinex processing, HSV space is implemented for CAMSHIFT tracker. All tested videos show performance improvement by using multiple object tracking precision (MOTP) index. The highest precision improvement is 60.35% while the least improvement is 2.92%. This algorithm can be further improved by using statistical decision method for more stable normalization.
| Original language | English |
|---|---|
| Pages (from-to) | 4699-4708 |
| Number of pages | 10 |
| Journal | Journal of Computational Information Systems |
| Volume | 10 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 1 Jun 2014 |
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Keywords
- CAMSHIFT
- Colour constancy
- Single object tracking
- White patch retinex
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems
Cite this
Improved white patch retinex colour constancy for robust single object tracker (CAMSHIFT). / Zulkifley, Mohd Asyraf; Wan Zaki, Wan Mimi Diyana; Hussain, Aini; Mustafa, Mohd. Marzuki.
In: Journal of Computational Information Systems, Vol. 10, No. 11, 01.06.2014, p. 4699-4708.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Improved white patch retinex colour constancy for robust single object tracker (CAMSHIFT)
AU - Zulkifley, Mohd Asyraf
AU - Wan Zaki, Wan Mimi Diyana
AU - Hussain, Aini
AU - Mustafa, Mohd. Marzuki
PY - 2014/6/1
Y1 - 2014/6/1
N2 - Single object tracker is a very important building block of an autonomous patient monitoring system. A good tracker will enable the system to determine the state of the patient accurately. A precise tracker will provide an accurate size of the tracked bounding box for patient analysis. However, illumination change always disrupts the tracker performance for both indoor and outdoor environments. The aim of this paper is to develop a tracker that is robust to illumination variation, especially towards uniform and sudden change. CAMSHIFT tracker has been selected as the base tracker where white patch retinex approach has been fused to. A histogram based decision is used for selecting the clipping pixel. While RGB space is used for retinex processing, HSV space is implemented for CAMSHIFT tracker. All tested videos show performance improvement by using multiple object tracking precision (MOTP) index. The highest precision improvement is 60.35% while the least improvement is 2.92%. This algorithm can be further improved by using statistical decision method for more stable normalization.
AB - Single object tracker is a very important building block of an autonomous patient monitoring system. A good tracker will enable the system to determine the state of the patient accurately. A precise tracker will provide an accurate size of the tracked bounding box for patient analysis. However, illumination change always disrupts the tracker performance for both indoor and outdoor environments. The aim of this paper is to develop a tracker that is robust to illumination variation, especially towards uniform and sudden change. CAMSHIFT tracker has been selected as the base tracker where white patch retinex approach has been fused to. A histogram based decision is used for selecting the clipping pixel. While RGB space is used for retinex processing, HSV space is implemented for CAMSHIFT tracker. All tested videos show performance improvement by using multiple object tracking precision (MOTP) index. The highest precision improvement is 60.35% while the least improvement is 2.92%. This algorithm can be further improved by using statistical decision method for more stable normalization.
KW - CAMSHIFT
KW - Colour constancy
KW - Single object tracking
KW - White patch retinex
UR - http://www.scopus.com/inward/record.url?scp=84904763762&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904763762&partnerID=8YFLogxK
U2 - 10.12733/jcis10351
DO - 10.12733/jcis10351
M3 - Article
VL - 10
SP - 4699
EP - 4708
JO - Journal of Computational Information Systems
JF - Journal of Computational Information Systems
SN - 1553-9105
IS - 11
ER -