Abstract
A direction research to visual content-driven videos has been in facilitating a short preview of each video through summarization that largely contains short-duration sequence combination of each scene corresponding to stationary camera. This work aims at using visual saliency features to trace scene-change positions in the video. In the present work, visual saliency features are built using color and intensity information as features. Further, using accumulated difference measure (Forward and Backward) in saliency features has been used to filter out false-positive scene-change outcomes. The results have been found to be quite satisfactory and provide closed match to the exact scene-change positions in the video. Significant accuracy is observed with videos using stationary cameras. For moving or non-stationary camera, video summarization has always been a challenging issue. The proposed method has been successfully tested over visual content-driven videos ranging from underwater scenes, fight sequences to surveillance videos in generating preview video.
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Ramya, G., Kulkarni, S. (2020). Visual Saliency Based Video Summarization: A Case Study For Preview Video Generation. In: Mandal, J., Bhattacharya, K., Majumdar, I., Mandal, S. (eds) Information, Photonics and Communication. Lecture Notes in Networks and Systems, vol 79. Springer, Singapore. https://doi.org/10.1007/978-981-32-9453-0_16
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DOI: https://doi.org/10.1007/978-981-32-9453-0_16
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