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• W4386901422 abstract "Video stabilization is one of the most interesting areas of research that can be implemented in consumer camera technology. Various works have been proposed to overcome this problem. However, there are many practical difficulties in deploying them in real time. Many frames are taken as an input to generate a stabilized output, which leads to high computation of the image sensing hardware. A novel approach to solve these challenges is proposed in this paper using the Hybrid Deep Neural Network Model (HDNNM) that uses gyroscopic sensor data and the optical flow analysis. This network is based on the deep unsupervised learning techniques, which require less computation and low buffer memory. The workflow of the proposed model is based on the integration of the optical flow with the joint motion representation to maintain a correspondence between the frames and the poses of scene objects. The LSTM module in the network wraps the grid and stabilizes the video frames. The relative motion along with the multi-stage training strategies was adopted to make the model unsupervised. The HDNNM is validated using the ablative dataset collected from various scenes, and the results of the model surpass the existing model’s performance metrics. To the best of the authors’ knowledge, this is the first unsupervised approach to overcome the challenges in video stabilization." @default.
• W4386901422 created "2023-09-21" @default.
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• W4386901422 date "2023-01-01" @default.
• W4386901422 modified "2023-09-27" @default.
• W4386901422 title "DIVS: A Real-Time Video Stabilization Framework for Consumer Camera" @default.
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• W4386901422 doi "https://doi.org/10.1007/978-981-99-4284-8_28" @default.
• W4386901422 hasPublicationYear "2023" @default.
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