https://scholars.lib.ntu.edu.tw/handle/123456789/489009
標題: | Combining multiple complementary features for pedestrian and motorbike detection. | 作者: | Wu, Cheng-En Chan, Yi-Ming LI-CHEN FU Hsiao, Pei-Yung Huang, Shih-Shinh Chen, Han-Hsuan Huang, Pang-Ting Hu, Shao-Chung |
公開日期: | 2013 | 起(迄)頁: | 1358-1363 | 來源出版物: | IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC | 會議論文: | 2013 16th International IEEE Conference on Intelligent Transportation Systems: Intelligent Transportation Systems for All Modes, ITSC 2013 | 摘要: | Pedestrian and motorbike detection are two important areas in obstacle detection on road. Most state-of-the-art detectors are constructed with new features or learning methods on Histograms of Oriented Gradients (HOG) features. However, few researches focus on analyzing which features are complementary for the aforementioned detection. According to our study of pedestrians and motorbikes, there are three major properties including shape, texture, and self-similarity. We design a Shape, Texture and Self-Similarity (STSS) feature for these properties. The features we have employed here are HOG, Local Oriented Pattern (LOP), Color Self-Similarity (CSS), and Texture Self-Similarity (TSS). The STSS detector which combines Shape, Texture, and Self-Similarty features achieves 31% log-average miss rate. At the same time, 93% detection rate at 10-4 false positives per window on INRIA Person Dataset has also been concluded. Besides, we also have evaluated our detector on Caltech Motorbike Dataset and Caltech Pedestrian Dataset, and found the detector outperforms HOG detector in these datasets. As a result, we have shown that these features are complement to each other and useful in pedestrian and motorbike detection. © 2013 IEEE. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/489009 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894337582&doi=10.1109%2fITSC.2013.6728420&partnerID=40&md5=2a4c61d3950911126944e91c969f055b |
DOI: | 10.1109/ITSC.2013.6728420 |
顯示於: | 資訊工程學系 |
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