Real-time detection and classification of arrow markings using curve-based prototype fitting

Maier, Georg; Pangerl, Sebastian; Schindler, Andreas

doi:10.1109/ivs.2011.5940451

Cited by 17 publications

(1 citation statement)

References 6 publications

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“…Early approaches for road marking classification [1], [2] proposed the matching of features/shapes to obtain the road marking classes from geometric primitives, but generally struggle with changes in orientation and scale. Additionally, these approaches performed shape classification independently, neglecting the fact that combinations of road markings are often found in the same spatial configuration.…”

Section: Introductionmentioning

confidence: 99%

Semantic Classification of Road Markings from Geometric Primitives

Amayo

Bruls

Newman

2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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The classification of semantically meaningful road markings in images is an important and safety critical task for autonomous and semi-autonomous vehicles. However, beyond simple lane markings, real-time detection and interpretation of road markings is challenging as images are subject to occlusions, partial observations, lighting changes and differing weather conditions. Additionally, there is high variation in the road markings between countries and regions, which makes interpretation difficult. In this work we present a threefold approach to the semantic classification. Firstly, we employ a weakly supervised neural network to detect pixels belonging to road markings under different conditions. Subsequently, these pixels are classified into geometric primitives, from which we retrieve the semantic classes through a fast and parallel modelfitting algorithm that offers real-time performance. Unlike other methods in the literature that perform road marking classification independently, our proposed approach performs a joint classification leveraging the highly structured configurations that characterise urban traffic scenes. Consequently, we retrieve the underlying semantic classes under a variety of weather and lighting conditions as we demonstrate in our results.

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Section: Introductionmentioning

confidence: 99%