Abstract
The now widely used object proposal methods for object detection commonly get fulfilling results on the dataset, which is captured in simple scenes. But the performance degraded when it comes to complicate real traffic scene. In our paper, a coarse to fine object proposal generating framework is proposed for autonomous driving object detection, provides a better object proposal solution in complex circumstances. By adding several low level geometrical features, which can be efficiently computed from binocular images, we recalculate scores for the candidate bounding boxes generated by coarse region proposal approaches with a Bayesian probability model. Our proposal generation approach is validated on the challenging KITTI benchmark, achieving state-of-art object proposal performance for pedestrian, car and cyclist.
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Liu, X., Cai, W., Liang, Z., Feng, Y. (2017). A Coarse to Fine Object Proposal Framework for Autonomous Driving Object Detection Using Binocular Image. In: Yuan, H., Geng, J., Bian, F. (eds) Geo-Spatial Knowledge and Intelligence. GRMSE 2016. Communications in Computer and Information Science, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-10-3969-0_25
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DOI: https://doi.org/10.1007/978-981-10-3969-0_25
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