19 January 2006 Fish tracking by combining motion based segmentation and particle filtering
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In this paper, we suggest a new importance sampling scheme to improve a particle filtering based tracking process. This scheme relies on exploitation of motion segmentation. More precisely, we propagate hypotheses from particle filtering to blobs of similar motion to target. Hence, search is driven toward regions of interest in the state space and prediction is more accurate. We also propose to exploit segmentation to update target model. Once the moving target has been identified, a representative model is learnt from its spatial support. We refer to this model in the correction step of the tracking process. The importance sampling scheme and the strategy to update target model improve the performance of particle filtering in complex situations of occlusions compared to a simple Bootstrap approach as shown by our experiments on real fish tank sequences.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
E. Bichot, E. Bichot, L. Mascarilla, L. Mascarilla, P. Courtellemont, P. Courtellemont, "Fish tracking by combining motion based segmentation and particle filtering", Proc. SPIE 6077, Visual Communications and Image Processing 2006, 607728 (19 January 2006); doi: 10.1117/12.650092; https://doi.org/10.1117/12.650092

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