Abstract
We present an approach for identifying and segmenting independently moving objects from dense scene flow information, using a moving stereo camera system. The detection and segmentation is challenging due to camera movement and non-rigid object motion. The disparity, change in disparity, and the optical flow are estimated in the image domain and the three-dimensional motion is inferred from the binocular triangulation of the translation vector. Using error propagation and scene flow reliability measures, we assign dense motion likelihoods to every pixel of a reference frame. These likelihoods are then used for the segmentation of independently moving objects in the reference image. In our results we systematically demonstrate the improvement using reliability measures for the scene flow variables. Furthermore, we compare the binocular segmentation of independently moving objects with a monocular version, using solely the optical flow component of the scene flow.
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Wedel, A., Meißner, A., Rabe, C., Franke, U., Cremers, D. (2009). Detection and Segmentation of Independently Moving Objects from Dense Scene Flow. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2009. Lecture Notes in Computer Science, vol 5681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03641-5_2
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DOI: https://doi.org/10.1007/978-3-642-03641-5_2
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