Robust and computationally efficient online image stabilisation framework based on adaptive dual motion vector integration

Autor: Muhammad Shehzad Hanif, Khurram Khurshid, Mahboob Iqbal, Adeel Yousaf, Muhammad Jaleed Khan
Rok vydání: 2019
Předmět:
Zdroj: IET Computer Vision. 13:461-468
ISSN: 1751-9640
DOI: 10.1049/iet-cvi.2018.5368
Popis: Image stabilisation aims to compensate and smoothen the effects of undesired trembling motion of cameras mounted on non-static platforms. It becomes quite a challenging task in the case of moving platforms, such as ground vehicles, unmanned aerial vehicles, and handheld devices. Many satisfactory solutions to the image stabilisation problem are proposed in the recent literature, but most of these methods are not adaptable for handling a wide range of intentional motions with minimum lag, especially in real-time scenarios. In this study, the authors propose an online two-dimensional image stabilisation technique based on dual motion vector integration, which is a novel adaptive motion smoothing technique that employs an average length of motion vectors to estimate the intentional motion. The overall computational cost of the proposed system is significantly reduced by employing frame-shaking judgment that only allows processing of jittering frames. Promising experimental results have been obtained on challenging videos obtained from hand-held and vehicle-mounted cameras which demonstrate the robustness and effectiveness of the proposed technique against feature point mismatching and presence of moving objects within the scene at a frame rate of 30 frames per second.
Databáze: OpenAIRE
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