Free-Space Detection with Self-Supervised and Online Trained Fully Convolutional Networks
| Autor: | Sanberg, Willem P., Dubbelman, Gijs, de With, Peter H. N. |
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| Rok vydání: | 2016 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: version as accepted at IS&T Electronic Imaging - Autonomous Vehicles and Machines Conference (San Francisco USA, January 2017); updated with two additional robustness experiments and formatted in conference style; 8 pages, public data available |
| Databáze: | arXiv |
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