AI-Based Cropping of Sport Videos Using SmartCrop
Permanent lenke
https://hdl.handle.net/10037/36766Dato
2024-08-27Type
Journal articleTidsskriftartikkel
Peer reviewed
Forfatter
Dorcheh, Sayed Mohammad Majidi; Houshmand Sarkhoosh, Mehdi; Midoglu, Cise; Sabet, Saeed Shafiee; Kupka, Tomas; Riegler, Michael Alexander; Johansen, Dag; Halvorsen, PålSammendrag
In the rapidly evolving landscape of digital platforms, the need for optimizing media representations to cater to various aspect ratios is palpable. In this paper, we pioneer an approach that utilizes object detection, scene detection, outlier detection, and interpolation for smart cropping. Using soccer as a case study, our primary goal is to capture the frame salience using object (player and ball) detection and tracking using AI models. To improve the object detection and tracking, we rely on scene understanding and explore various outlier detection and interpolation techniques. Our pipeline, called SmartCrop, is efficient, and supports various configurations for object tracking, interpolation, and outlier detection to find the best point-of-interest to be used as the cropping center of the video frame. An objective evaluation of the performance of individual pipeline components has validated our proposed architecture. Moreover, a crowdsourced subjective user study, assessing the alternative approaches for cropping from 16:9 to 1:1 and 9:16 aspect ratios, confirms that our proposed approach increases the end-user quality of experience.
Forlag
World Scientific PublishingSitering
Dorcheh, Houshmand Sarkhoosh, Midoglu, Sabet, Kupka, Riegler, Johansen, Halvorsen. AI-Based Cropping of Sport Videos Using SmartCrop. International Journal of Semantic Computing (IJSC). 2024;18(4):637-662Metadata
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