Fast Electronic Digital Image Stabilization for Off-Road Navigation

Morimoto, Carlos H.; Chellappa, Rama

doi:10.1006/rtim.1996.0030

Cited by 64 publications

(29 citation statements)

References 8 publications

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“…Alternatively, one can capture the image as is (without stabilization) and use digital postprocessing to correct the motion blurred image [27,12]. This has been demonstrated to be effective in stabilizing video sequences captured from moving vehicles [15,18].…”

Section: Related Workmentioning

confidence: 99%

Active gaze stabilization

Lesmana¹,

Landgren

Forssén

et al. 2014

Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

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We describe a system for active stabilization of cameras mounted on highly dynamic robots. To focus on careful performance evaluation of the stabilization algorithm, we use a camera mounted on a robotic test platform that can have unknown perturbations in the horizontal plane, a commonly occurring scenario in mobile robotics. We show that the camera can be effectively stabilized using an inertial sensor and a single additional motor, without a joint position sensor. The algorithm uses an adaptive controller based on a model of the vertebrate Cerebellum for velocity stabilization, with additional drift correction. We have also developed a resolution adaptive retinal slip algorithm that is robust to motion blur.We evaluated the performance quantitatively using another high speed robot to generate repeatable sequences of large and fast movements that a gaze stabilization system can attempt to counteract. Thanks to the high-accuracy repeatability, we can make a fair comparison of algorithms for gaze stabilization. We show that the resulting system can reduce camera image motion to about one pixel per frame on average even when the platform is rotated at 200 degrees per second. As a practical application, we also demonstrate how the common task of face detection benefits from active gaze stabilization.

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Section: Related Workmentioning

confidence: 99%

Active gaze stabilization

Lesmana¹,

Landgren

Forssén

et al. 2014

Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

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show abstract

“…Different models have been used depending on the nature of the vibrations: 2D [31], [32,33], 2.5D [34] or full 3D [35]. In general it can be observed that motion estimation based on high degree models gives better accuracy (as is to be expected) but at significant computation cost and potential instability [31][32][33][34][35]. By contrast we propose a somewhat simpler model that, whilst risking being naively considered as ill-suited to the task, can be further enabled by a novel approach of robust motion filtering.…”

Section: Motion Estimation and Filteringmentioning

confidence: 99%

Robust motion filtering as an enabler to video stabilization for a tele-operated mobile robot

Chereau

Breckon

2013

SPIE Proceedings

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An increasing number of inspection and hazardous environment tasks use mobile robotic vehicles manually tele-operated via a live video feed from an on-board camera. The resulting video imagery frequently suffers from vibration artefacts compromising the accuracy and security of operation in addition to the viable duration for human tele-operation. Here we aim to automatically remove these unwanted visual effects using a novel real-time video stabilization approach. Prior work for hand-held and vehicle mounted cameras is ill-suited to the high-frequency, large magnitude (10-15% of image size) vibration encountered on the short wheelbase, non-suspended robotic platforms typically deployed for such tasks.Without prior knowledge of the robot ego-motion (or vibration characteristics) we develop a novel four stage filtering approach to identify robust Local Motion Vectors (LMV) for Global Motion Vector (GMV) estimation in successive video frames whilst preserving the required real-time responsiveness for tele-operation. Experimental results over a range of tele-operation scenarios show that the method provides both significant qualitative visual improvement and a quantitative reduction in measurable video image displacement (caused by vibration).

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“…Since the unstable component of the camera motion is combined with the component that results from smooth self-motion, traditional stabilization techniques estimate the full camera motion and remove or smooth it afterwards [4]. Camera motion can be decomposed into a 3D translation and a 3D rotation.…”

Section: Existing Stabilization Techniquesmentioning

confidence: 99%

Optic flow from unstable sequences through local velocity constancy maximization

Pauwels

Hulle

2009

Image and Vision Computing

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We introduce a novel video stabilization method that enables the extraction of optic flow from short unstable sequences. Contrary to traditional stabilization techniques that use approximative global motion models to estimate the full camera motion, our method estimates the unstable component of the camera motion only. This allows for the use of simpler global motion models, and at the same time extends the validity to more complex environments, such as close scenes that contain independently moving objects. The unstable component of the camera motion is derived from a maximization of the temporal local velocity constancy over the entire short sequence. The method, embedded within a phase-based optic flow algorithm, is tested on both synthetic and complex real-world sequences. The optic flow obtained using our technique is denser than that extracted directly from the original sequence, and from a sequence stabilized with a more traditional stabilization technique.

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Fast Electronic Digital Image Stabilization for Off-Road Navigation

Cited by 64 publications

References 8 publications

Active gaze stabilization

Active gaze stabilization

Robust motion filtering as an enabler to video stabilization for a tele-operated mobile robot

Optic flow from unstable sequences through local velocity constancy maximization

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