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In recent years, data-driven methods have shown great success for extractinginformation about the infrastructure in urban areas. These algorithms areusually trained on large datasets consisting of thousands or millions oflabeled training examples. While large datasets have been published regardingcars, for cyclists very few labeled data is available although appearance,point of view, and positioning of even relevant objects differ. Unfortunately,labeling data is costly and requires a huge amount of work. In this paper, wethus address the problem of learning with very few labels. The aim is torecognize particular traffic signs in crowdsourced data to collect informationwhich is of interest to cyclists. We propose a system for object recognitionthat is trained with only 15 examples per class on average. To achieve this, wecombine the advantages of convolutional neural networks and random forests tolearn a patch-wise classifier. In the next step, we map the random forest to aneural network and transform the classifier to a fully convolutional network.Thereby, the processing of full images is significantly accelerated andbounding boxes can be predicted. Finally, we integrate data of the GlobalPositioning System (GPS) to localize the predictions on the map. In comparisonto Faster R-CNN and other networks for object recognition or algorithms fortransfer learning, we considerably reduce the required amount of labeled data.We demonstrate good performance on the recognition of traffic signs forcyclists as well as their localization in maps.

Object Recognition from very few Training Examples for Enhancing Bicycle Maps