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Human-oriented video retargeting via object detection and patch decision

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Abstract

In this study, we suggest a novel video retargeting approach by considering the essential factors of a video: main object and movement thereof. Such two factors have been considered including the region of interest (ROI) for target object. Experimentally, we set the main object as human for storing the interaction object and movement in each sequential frame. Our method aims to preserve the ROI to the maximum extent possible over retargeting constraints for the target resolution. With a view to preserving the original main object, we rely on an object detection model to identify human-oriented objects; subsequently, we conduct a decision-making process to determine the suitability of our scheme. Upon the application of the proposed method, video frames are split into many patches and then generated with a precise target resolution using a video super-resolution model. The results of retargeting the frame images are compared against quality assessment metrics. The PSNR, SSIM, MS-SSIM, LPIPS, BMPRI, BRISQUE, PIQE and NIQE were used. We perform comparative experiments to confirm that the proposed approach can maintain the original ratio of important objects and the content of the video. We experimentally demonstrate that the proposed approach could enhance video resolution while ensuring visually pleasing quality and original important object.

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Data availability

Two datasets: DAVIS [19] and TVD [30] are used in this research. The DAVIS [19] dataset can be obtained at https://davischallenge.org/davis2017/code.html, and TVD [30] dataset can be accessed at https://multimedia.tencent.com/resources/tvd. The results of retargeting videos can be accessed at http://gofile.me/7apVR/aNiuR7y1d.

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Acknowledgements

This work was supported in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2021-0-00087, Development of high-quality conversion technology for SD/HD low-quality media) and in part by the BK21 FOUR project (AI-driven Convergence Software Education Research Program) funded by the Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (4199990214394).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, Korea

    Dong-Hwi Kim, Sujin Lee, Jaehyun Bae & Sang-hyo Park

  2. Electronics and Telecommunications Research Institute (ETRI), Daejeon, 34129, Korea

    Sukee Cho & Byungjun Bae

  3. Korea Education & Research Information Service (KERIS), Daegu, 41061, Korea

    Jieun Lee

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  1. Dong-Hwi Kim
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Kim, DH., Lee, S., Bae, J. et al. Human-oriented video retargeting via object detection and patch decision. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18878-6

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