2011
DOI: 10.1364/ao.50.006302
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Detection and tracking of sea-surface targets in infrared and visual band videos using the bag-of-features technique with scale-invariant feature transform

Abstract: Sea-surface targets are automatically detected and tracked using the bag-of-features (BOF) technique with the scale-invariant feature transform (SIFT) in infrared (IR) and visual (VIS) band videos. Features corresponding to the sea-surface targets and background are first clustered using a training set offline, and these features are then used for online target detection using the BOF technique. The features corresponding to the targets are matched to those in the subsequent frame for target tracking purposes … Show more

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Cited by 21 publications
(15 citation statements)
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“…Can et al [7] proposed to use Scale Invariant Feature Transform (SIFT) features with the Bag-of-Features (BOF) technique for detection and tracking of sea-surface targets in infrared (IR) and visual band video streams. They used the K-means algorithm to generate clusters in the visual band.…”
Section: Segmentationmentioning
confidence: 99%
See 1 more Smart Citation
“…Can et al [7] proposed to use Scale Invariant Feature Transform (SIFT) features with the Bag-of-Features (BOF) technique for detection and tracking of sea-surface targets in infrared (IR) and visual band video streams. They used the K-means algorithm to generate clusters in the visual band.…”
Section: Segmentationmentioning
confidence: 99%
“…Other metrics have been used such as the ones presented by Can et al [7] which assessed the performance of tracking algorithm. Although tracking performance is beyond the scope of this research project, some of these metrics could potentially be used  Metric 4 (M4): M4 defined the true positive rate, which is calculated as the ratio between the correctly detected target area and the whole detected target area.…”
Section: Tpr Ppvmentioning
confidence: 99%
“…2D optical flow is commonly used as a feature in motion-based segmentation and tracking applications (e.g., [21]). Surprisingly, visual-motion research on tracking applications have rarely exploited scene flow information.…”
Section: Scene Flowmentioning
confidence: 99%
“…Since the background clutter is extremely complex and strong, the target seems quite small and is easily drowned out by disturbances [4][5][6]. Finally, although a number of algorithms have been presented for small IR target tracking in specific situations, e.g., sea clutter during the last decades, many of these trackers may fail in different circumstances [7,8]. For example, a tracker may be good in coping with sea-wave clutter but has difficulty in handling cluttered sky-cloud situations.…”
Section: Introductionmentioning
confidence: 99%