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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Cheng-En Wu Yi-Ming Chan Li-Chen Fu Pei-Yung Hsiao Shin-Shinh Huang Han-Hsuan Chen Pang-Ting Huang Shao-Chung Hu |
| Copyright Year | 2013 |
| Description | Author affiliation: Chung-Shan Inst. of Sci. & Technol., Longtan, Taiwan (Shao-Chung Hu) || Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan (Cheng-En Wu; Yi-Ming Chan; Li-Chen Fu; Han-Hsuan Chen; Pang-Ting Huang) || Dept. of Comput. & Commun. Eng., Nat. Kaohsiung First Univ. of Sci. & Technol., Kaohsiung, Taiwan (Shin-Shinh Huang) || Dept. of Electr. Eng., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan (Pei-Yung Hsiao) |
| Abstract | 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. |
| Sponsorship | IEEE Intell.Transp. Syst. Soc. |
| Starting Page | 1358 |
| Ending Page | 1363 |
| File Size | 2002215 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479929146 |
| DOI | 10.1109/ITSC.2013.6728420 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-10-06 |
| Publisher Place | Netherlands |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Detectors |
| Content Type | Text |
| Resource Type | Article |
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