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- W2338103624 abstract "Recently, vision-based Advanced Driver Assist Systems have gained broad interest. In this work, we investigate free-space detection, for which we propose to employ a Fully Convolutional Network (FCN). We show that this FCN can be trained in a self-supervised manner and achieve similar results compared to training on manually annotated data, thereby reducing the need for large manually annotated training sets. To this end, our self-supervised training relies on a stereo-vision disparity system, to automatically generate (weak) training labels for the color-based FCN. Additionally, our self-supervised training facilitates online training of the FCN instead of offline. Consequently, given that the applied FCN is relatively small, the free-space analysis becomes highly adaptive to any traffic scene that the vehicle encounters. We have validated our algorithm using publicly available data and on a new challenging benchmark dataset that is released with this paper. Experiments show that the online training boosts performance with 5% when compared to offline training, both for Fmax and AP." @default.
- W2338103624 created "2016-06-24" @default.
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- W2338103624 date "2016-04-08" @default.
- W2338103624 modified "2023-09-27" @default.
- W2338103624 title "Free-Space Detection with Self-Supervised and Online Trained Fully Convolutional Networks" @default.
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- W2338103624 hasPublicationYear "2016" @default.
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