Abstract
Vision-based environment perception is particularly challenging in bad weather. Under such conditions, even most powerful stereo algorithms suffer from highly correlated, ”blob”-like noise, that is hard to model. In this paper we focus on extending an existing stereo-based scene representation – the Stixel World – to allow its application even under problematic conditions. To this end, we estimate the probability of existence for each detected obstacle. Results show that the amount of false detections can be reduced significantly by demanding temporal consistency of the representation and by analyzing cues that represent the geometry of typical obstacles.
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Scharwächter, T. (2013). Stixel-Based Target Existence Estimation under Adverse Conditions. 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_23
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DOI: https://doi.org/10.1007/978-3-642-40602-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40601-0
Online ISBN: 978-3-642-40602-7
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