Abstract
Precise stereo-based depth estimation at large distances is challenging: objects become very small, often exhibit low contrast in the image, and can hardly be separated from the background based on disparity due to measurement noise. In this paper we present an approach that overcomes these problems by combining robust object segmentation and highly accurate depth and motion estimation. The segmentation criterion is formulated as a probabilistic combination of disparity, optical flow and image intensity that is optimized using graph cuts. Segmentation and segment parameter models for the different cues are iteratively refined in an Expectation-Maximization scheme. Experiments on real-world traffic scenes demonstrate the accuracy of segmentation and disparity results for vehicles at distances of up to 180 meters. The proposed approach outperforms state-of-the-art stereo methods, achieving an average object disparity RMS error below 0.1 pixel, at typical object sizes of less than 15x15 pixels.
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Pinggera, P., Franke, U., Mester, R. (2013). Highly Accurate Depth Estimation for Objects at Large Distances. In: Weickert, J., Hein, M., Schiele, B. (eds) Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, vol 8142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40602-7_3
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DOI: https://doi.org/10.1007/978-3-642-40602-7_3
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