Enyu Du, Ning Zhang, Yandi Li. Multi Classification Method of Lane Arrow Markings Based on Support Vector Machines with Adaptive Partitioning Coding[J]. Acta Optica Sinica, 2018, 38(10): 1015003
- Acta Optica Sinica
- Vol. 38, Issue 10, 1015003 (2018)
Fig. 1. Original image
Fig. 2. ow chart of segmentation recognition area
Fig. 3. ROI of arrow marking
Fig. 4. Results of Harris corner detection
Fig. 5. Principle of FAST-9 algorithm
Fig. 6. Detection results of original FAST-9 algorithm
Fig. 7. Precision detection process and results with improved FAST-9 algorithm. (a) Process of precision detection; (b) results of precision detection
Fig. 8. Arrow marking recognition areas. (a) Straight or left; (b) straight; (c) right
Fig. 9. Partial positive and negative sample images. (a) Positive samples; (b) negative samples
Fig. 10. Probability distributions of the first three steps moments of positive and negative samples. (a) First order; (b) second order; (c) third order
Fig. 11. Feature vector distribution
Fig. 12. Illustrative diagram of SVM multi classification method
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Table 1. Binary encoding table of arrow markings
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Table 2. Classification accuracy for arrow markings
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Table 3. Results of algorithm evaluation
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