Abstract
Most large public datasets containing cyclists for training detectors based on Deep Learning have annotations for bicycles and people, but not for cyclists. Even when it is not the case, the quality and quantity of the images are limited. To overcome these limitations, we propose the new OpenImages Cyclists dataset, built through the pre-selection of images from the OpenImages set and a new algorithm for semiautomatic generation of cyclist annotation aided by people and bicycle detectors. A cyclist detector trained with this dataset achieved identification rates up to 78% and 89% in two different sets of images obtained from security cameras at USP, Campus São Paulo - Capital.
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