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Dynamic visual saliency in image sequences

Abstract : Our thesis research is concerned with the estimation of motion saliency in image sequences. First, we have defined an original method to detect frames in which a salient motion is present. For this, we propose a framework relying on a deep neural network, and on the compensation of the dominant camera motion. Second, we have designed a method for estimating motion saliency maps. This method requires no learning. The motion saliency cue is obtained by an optical flow inpainting step, followed by a comparison with the initial flow. Third, we consider the problem of trajectory saliency estimation to handle progressive saliency over time. We have built a weakly supervised framework based on a recurrent auto-encoder that represents trajectories with latent codes. Performance of the three methods was experimentally assessed on real video datasets.
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Submitted on : Wednesday, March 10, 2021 - 9:57:10 AM
Last modification on : Thursday, January 20, 2022 - 4:12:29 PM


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  • HAL Id : tel-03087274, version 2



Léo Maczyta. Dynamic visual saliency in image sequences. Signal and Image processing. Université Rennes 1, 2020. English. ⟨NNT : 2020REN1S046⟩. ⟨tel-03087274v2⟩



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