A Central Sub-image Based Global Motion Estimation Method for In-Car Video Stabilization

Zhang, Yuefei; Xie, Mei; Tang, Dongming

doi:10.1109/wkdd.2010.112

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…The proposed method requires prior information of road lines, which is a major drawback. Zhang et al [46] proposed block features‐based image stabilisation for in‐car videos. However, this method did not achieve notable performance in the presence of a complex background.…”

Section: Related Workmentioning

confidence: 99%

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

Yousaf

Hanif

Khan

et al. 2019

IET Computer Vision

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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.

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Section: Related Workmentioning

confidence: 99%

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

Yousaf

Hanif

Khan

et al. 2019

IET Computer Vision

View full text Add to dashboard Cite

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Deep learning serves traffic safety analysis: A forward‐looking review

Razi

Chen

et al. 2022

IET Intelligent Trans Sys

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This paper explores deep learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasising driving safety for both autonomous vehicles and human‐operated vehicles. A typical processing pipeline is presented, which can be used to understand and interpret traffic videos by extracting operational safety metrics and providing general hints and guidelines to improve traffic safety. This processing framework includes several steps, including video enhancement, video stabilisation, semantic and incident segmentation, object detection and classification, trajectory extraction, speed estimation, event analysis, modelling, and anomaly detection. The main goal is to guide traffic analysts to develop their own custom‐built processing frameworks by selecting the best choices for each step and offering new designs for the lacking modules by providing a comparative analysis of the most successful conventional and DL‐based algorithms proposed for each step. Existing open‐source tools and public datasets that can help train DL models are also reviewed. To be more specific, exemplary traffic problems are reviewed and required steps are mentioned for each problem. Besides, connections to the closely related research areas of drivers' cognition evaluation, crowd‐sourcing‐based monitoring systems, edge computing in roadside infrastructures, automated driving systems‐equipped vehicles are investigated, and the missing gaps are highlighted. Finally, commercial implementations of traffic monitoring systems, their future outlook, and open problems and remaining challenges for widespread use of such systems are reviewed.

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Research on Multi-depth-of-field Electronic Image Stabilization Algorithm for In-Vehicle Videos

Qiu

Liang

Wang

et al. 2022

2022 5th International Conference on Robotics, Control and Automation Engineering (RCAE)

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A Central Sub-image Based Global Motion Estimation Method for In-Car Video Stabilization

Cited by 6 publications

References 10 publications

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

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

Deep learning serves traffic safety analysis: A forward‐looking review

Research on Multi-depth-of-field Electronic Image Stabilization Algorithm for In-Vehicle Videos

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