Pedestrian Detection Using Regression-Based Feature Selection and Disparity Map
Issued Date
2019-12-18
Citation
Lee, Chung-Hee. (2019-12-18). Pedestrian Detection Using Regression-Based Feature Selection and Disparity Map. 11th International Conference on Computer Science and its Applications, CSA 2019 & 14th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2019, 515–520. doi: 10.1007/978-981-15-9343-7_72
Type
Conference Paper
ISBN
9789811593420
ISSN
1876-1100
Abstract
In this paper, the pedestrian detection using a regression-based feature selection and a disparity map method is proposed for improving the processing speed. Using many features helps to improve detection performance, but slows down processing. Therefore, it is important to select and use features efficiently. Our proposed method consists of three stages, such as a disparity map-based detection stage, a segmentation stage using a transformed disparity map, and a recognition stage with regression-based feature analysis. Through experiments with the ETH database, we show that the proposed method improves detection performance and especially processing speed.
