| Presentation | 2017-10-13 A Study on Traffic Sign Detection and Classification with Single Shot Detection Janet Mardjuki, Yongqing Sun, Shingo Ando, Kinebuchi Tetsuya, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | For this paper, we would be presenting our basic investigation on real life traffic-sign detection and classification in the form of images. We applied one of the latest states of art method in object detection on the public dataset that was submitted to Conference on Computer Vision and Pattern Recognition (CVPR) in 2016. The dataset we would be working was submitted as part of the paper "Traffic-Sign Detection and Classification in the Wild". Unlike various dataset that was available in the past, this traffic sign dataset could represent the images encountered in real life. For their own experiment, they used RCNN to detect and classify the traffic sign, which is not the latest and fastest method that is currently available. Thus, we decided to conduct some experiments with the dataset using different method, Single Shot Multi Detector, which is one of the most accurate and fastest methods that are currently available. In this paper, we will describe our observation on several experiment conducted to optimize the detection speed and the accuracy for this particular dataset. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Object detectionSSDDeep learningTraffic sign detection |
| Paper # | PRMU2017-96 |
| Date of Issue | 2017-10-05 (PRMU) |
| Conference Information | |
| Committee | PRMU |
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| Conference Date | 2017/10/12(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Shinichi Sato(NII) |
| Vice Chair | Hironobu Fujiyoshi(Chubu Univ.) / Yoshihisa Ijiri(Omron) |
| Secretary | Hironobu Fujiyoshi(AIST) / Yoshihisa Ijiri(NAIST) |
| Assistant | Masato Ishii(NEC) / Yusuke Sugano(Osaka Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Pattern Recognition and Media Understanding |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study on Traffic Sign Detection and Classification with Single Shot Detection |
| Sub Title (in English) | |
| Keyword(1) | Object detectionSSDDeep learningTraffic sign detection |
| 1st Author's Name | Janet Mardjuki |
| 1st Author's Affiliation | Simon Fraser University, Canada(Simon Fraser Univ.) |
| 2nd Author's Name | Yongqing Sun |
| 2nd Author's Affiliation | NTT Media Intelligence Laboratories(NTT) |
| 3rd Author's Name | Shingo Ando |
| 3rd Author's Affiliation | NTT Media Intelligence Laboratories(NTT) |
| 4th Author's Name | Kinebuchi Tetsuya |
| 4th Author's Affiliation | NTT Media Intelligence Laboratories(NTT) |
| Date | 2017-10-13 |
| Paper # | PRMU2017-96 |
| Volume (vol) | vol.117 |
| Number (no) | PRMU-238 |
| Page | pp.pp.187-191(PRMU), |
| #Pages | 5 |
| Date of Issue | 2017-10-05 (PRMU) |