Abstract
It is common in oculomotor and vestibular research to use video or still cameras to acquire data on eye movements. Unfortunately, such data are often contaminated by unwanted motion of the face relative to the camera, especially during experiments in dynamic motion environments. We develop a method for estimating the motion of a camera relative to a highly deformable surface, specifically the movement of a camera relative to the face and eyes. A small rectangular region of interest (ROI) on the face is automatically selected and tracked throughout a set of video frames as a measure of vertical camera translation. The specific goal is to present a process based on a genetic algorithm that selects a suitable ROI for tracking: one whose translation within the camera image accurately matches the actual relative motion of the camera. We find that co-correlation, a statistic describing the time series of a large group of ROIs, predicts the accuracy of the ROIs, and can be used to select the best ROI from a group. After the genetic algorithm finds the best ROIs from a group, it uses recombination to form a new generation of ROIs that inherit properties of the ROIs from the previous generation. We show that the algorithm can select an ROI that will estimate camera translation and determine the direction that the eye is looking with an average accuracy of 0.75°, even with camera translations of 2.5 mm at a viewing distance of 120 mm, which would cause an error of 11° without correction.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 157-165 |
| Number of pages | 9 |
| Journal | Journal of Neuroscience Methods |
| Volume | 176 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jan 30 2009 |
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Keywords
- Cross-correlation
- Eye
- Genetic algorithm
- Video
- Video-oculography
- VOG
ASJC Scopus subject areas
- Neuroscience(all)
Cite this
Compensating for camera translation in video eye-movement recordings by tracking a representative landmark selected automatically by a genetic algorithm. / Karmali, Faisal; Shelhamer, Mark J.
In: Journal of Neuroscience Methods, Vol. 176, No. 2, 30.01.2009, p. 157-165.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Compensating for camera translation in video eye-movement recordings by tracking a representative landmark selected automatically by a genetic algorithm
AU - Karmali, Faisal
AU - Shelhamer, Mark J
PY - 2009/1/30
Y1 - 2009/1/30
N2 - It is common in oculomotor and vestibular research to use video or still cameras to acquire data on eye movements. Unfortunately, such data are often contaminated by unwanted motion of the face relative to the camera, especially during experiments in dynamic motion environments. We develop a method for estimating the motion of a camera relative to a highly deformable surface, specifically the movement of a camera relative to the face and eyes. A small rectangular region of interest (ROI) on the face is automatically selected and tracked throughout a set of video frames as a measure of vertical camera translation. The specific goal is to present a process based on a genetic algorithm that selects a suitable ROI for tracking: one whose translation within the camera image accurately matches the actual relative motion of the camera. We find that co-correlation, a statistic describing the time series of a large group of ROIs, predicts the accuracy of the ROIs, and can be used to select the best ROI from a group. After the genetic algorithm finds the best ROIs from a group, it uses recombination to form a new generation of ROIs that inherit properties of the ROIs from the previous generation. We show that the algorithm can select an ROI that will estimate camera translation and determine the direction that the eye is looking with an average accuracy of 0.75°, even with camera translations of 2.5 mm at a viewing distance of 120 mm, which would cause an error of 11° without correction.
AB - It is common in oculomotor and vestibular research to use video or still cameras to acquire data on eye movements. Unfortunately, such data are often contaminated by unwanted motion of the face relative to the camera, especially during experiments in dynamic motion environments. We develop a method for estimating the motion of a camera relative to a highly deformable surface, specifically the movement of a camera relative to the face and eyes. A small rectangular region of interest (ROI) on the face is automatically selected and tracked throughout a set of video frames as a measure of vertical camera translation. The specific goal is to present a process based on a genetic algorithm that selects a suitable ROI for tracking: one whose translation within the camera image accurately matches the actual relative motion of the camera. We find that co-correlation, a statistic describing the time series of a large group of ROIs, predicts the accuracy of the ROIs, and can be used to select the best ROI from a group. After the genetic algorithm finds the best ROIs from a group, it uses recombination to form a new generation of ROIs that inherit properties of the ROIs from the previous generation. We show that the algorithm can select an ROI that will estimate camera translation and determine the direction that the eye is looking with an average accuracy of 0.75°, even with camera translations of 2.5 mm at a viewing distance of 120 mm, which would cause an error of 11° without correction.
KW - Cross-correlation
KW - Eye
KW - Genetic algorithm
KW - Video
KW - Video-oculography
KW - VOG
UR - http://www.scopus.com/inward/record.url?scp=57649171510&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=57649171510&partnerID=8YFLogxK
U2 - 10.1016/j.jneumeth.2008.09.010
DO - 10.1016/j.jneumeth.2008.09.010
M3 - Article
C2 - 18835407
AN - SCOPUS:57649171510
VL - 176
SP - 157
EP - 165
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
SN - 0165-0270
IS - 2
ER -