Abstract
This paper presents a new circular object detection method based on geometric property and polynomial fitting in polar coordinates instead of implementing it in Cartesian coordinates for 2-Dimension (2D) lidar data. There are three procedures of the algorithm. Firstly, a simple and fast segmentation method is proposed. Then, according to the circle property, five robust and effective features in natural lidar coordinates for each segment are defined. Finally, these features are normalized and fed into Support Vector Machine (SVM) to detect the target circular object. Three videos containing 1330 frames data are manually labeled and used to test the performance of the proposed algorithm. The best accuracy is 99.79 % and the execution time is lower than 16.93 ms. Experimental results demonstrate that circular object can be detected efficiently and accurately by the proposed method.
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Notes
- 1.
Videos and codes download: https://yunpan.cn/cSngC6y6rjZ8I, download-code: 6242.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant (No. 61175075, No. 61573134), the National Science and technology support program (No. 2015BAF11B01).
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Zhou, X., Wang, Y., Zhu, Q., Miao, Z. (2016). Circular Object Detection in Polar Coordinates for 2D LIDAR Data. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_6
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