Abstract
Fixed video camera systems are consistently prone to importune motions over time due to either thermal effects or mechanical factors. Even subtle displacements are mostly overlooked or ignored, although they can lead to large geo-rectification errors. This paper describes a simple and efficient method to stabilize an either continuous or sub-sampled image sequence based on feature matching and sub-pixel cross-correlation techniques. The method requires the presence and identification of different land-sub-image regions containing static recognizable features, such as corners or salient points, referred to as keypoints. A Canny edge detector ( C E D ) is used to locate and extract the boundaries of the features. Keypoints are matched against themselves after computing their two-dimensional displacement with respect to a reference frame. Pairs of keypoints are subsequently used as control points to fit a geometric transformation in order to align the whole frame with the reference image. The stabilization method is applied to five years of daily images collected from a three-camera permanent video system located at Anglet Beach in southwestern France. Azimuth, tilt, and roll deviations are computed for each camera. The three cameras showed motions on a wide range of time scales, with a prominent annual signal in azimuth and tilt deviation. Camera movement amplitude reached up to 10 pixels in azimuth, 30 pixels in tilt, and 0.4° in roll, together with a quasi-steady counter-clockwise trend over the five-year time series. Moreover, camera viewing angle deviations were found to induce large rectification errors of up to 400 m at a distance of 2.5 km from the camera. The mean shoreline apparent position was also affected by an approximately 10–20 m bias during the 2013/2014 outstanding winter period. The stabilization semi-automatic method successfully corrects camera geometry for fixed video monitoring systems and is able to process at least 90% of the frames without user assistance. The use of the C E D greatly improves the performance of the cross-correlation algorithm by making it more robust against contrast and brightness variations between frames. The method appears as a promising tool for other coastal imaging applications such as removal of undesired high-frequency movements of cameras equipped in unmanned aerial vehicles (UAVs).
Highlights
Over the past decades, the use of shore-based video systems has become a very popular and accessible low-cost tool for coastal monitoring, given their capability to deliver continuous, high-frequency data over large enough spatial scales [1,2]
The method appears as a promising tool for other coastal imaging applications such as removal of undesired high-frequency movements of cameras equipped in unmanned aerial vehicles (UAVs)
A common approach consists of using projective transformation [20,21] that usually takes into account two types of calibration: An intrinsic calibration, which accounts for the physical characteristics of the camera lens and can be obtained directly in the lab prior to field installation, and an extrinsic calibration, which depends on the camera location and orientation after installation, as well as a set of surveyed ground control points (GCPs), correspondingly manually digitized from the image
Summary
The use of shore-based video systems has become a very popular and accessible low-cost tool for coastal monitoring, given their capability to deliver continuous, high-frequency (e.g., daily) data over large enough spatial scales [1,2]. Successful and reliable video-based products can only be produced if accurate image transformation into real-world coordinates is achieved. A common approach consists of using projective transformation [20,21] that usually takes into account two types of calibration: An intrinsic calibration, which accounts for the physical characteristics of the camera lens and can be obtained directly in the lab prior to field installation (in order to remove distortion effects), and an extrinsic calibration, which depends on the camera location and orientation after installation, as well as a set of surveyed ground control points (GCPs), correspondingly manually digitized from the image Both calibrations are often done just once, assuming that the physical properties of the lens remain unchanged over time and that the video cameras and their mounting structures do not move.
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