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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Uhang He hi Chen Ifeng Pan Ai Ni |
| Copyright Year | 2014 |
| Description | Author affiliation: Sch. of Comput. Sci., Wuhan Univ., Wuhan, China (hi Chen) || Inst. of Deep Learning (IDL), Baidu Inc., Beijing, China (Uhang He; Ifeng Pan; Ai Ni) |
| Abstract | Arrow road markings usually appear on freeway surface and they convey important navigation information to autonomous driving. But detecting and recognizing them is a tough task because they suffer from numerous deviations like objects' interference and themselves' abrasion, etc. We therefore propose a novel local junction feature (L-junction) to describe each road marking as a junction string, different deviation is dispersed into different junction. We encode those junctions within a range as the same code. To measure the similarity between detected junction string and ground truth junction string, we design a weighted edit distance strategy and assign different deviation with different weight so that our framework is robust enough to deviations in arrow road marking but sensitive to non- arrow road markings' deviations. To test our framework, we collect three freeway datasets with our self-driving car: clean/dirty arrow road marking images (300 images respectively), a video dataset (arrow road marking and non-road marking images (670 images)). Another deep learning framework (Boosting+Convolutional Deep Neural Network (CDNN)) is also implemented for comparison. Extensive experimental results well demonstrate the superior performance of our framework. |
| Starting Page | 2317 |
| Ending Page | 2323 |
| File Size | 2258657 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781479960781 |
| DOI | 10.1109/ITSC.2014.6958061 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-10-08 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Junctions Roads Encoding Topology Image recognition Feature extraction Boosting |
| Content Type | Text |
| Resource Type | Article |
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