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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kai Yang Chao Liu Jiang Yu Zheng Christopher, L. Yaobin Chen |
| Copyright Year | 2014 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Indiana Univ. Purdue Univ. Indianapolis, Indianapolis, IN, USA (Kai Yang; Chao Liu; Christopher, L.) || Dept. of Comput. Sci., Indiana Univ. Purdue Univ., Indianapolis, IN, USA (Jiang Yu Zheng) |
| Abstract | Monocular based bicyclist detection in naturalistic driving video is a very challenging problem due to the high variance of the bicyclist appearance and complex background of naturalistic driving environment. In this paper, we propose a two-stage multi-modal bicyclist detection scheme to efficiently detect bicyclists with varied poses for further behavior analysis. A new motion based region of interest (ROI) detection is first applied to the entire video to refine the region for sliding-window detection. Then an efficient integral feature based detector is applied to quickly filter out the negative windows. Finally, the remaining candidate windows are encoded and tested by three pre-learned pose-specific detectors. The experimental results on our TASI 110 car naturalistic driving dataset show the effectiveness and efficiency of the proposed method. The proposed method outperforms the traditional. |
| Starting Page | 1638 |
| Ending Page | 1643 |
| File Size | 484557 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479960781 |
| DOI | 10.1109/ITSC.2014.6957928 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-10-08 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Conferences Intelligent transportation systems |
| Content Type | Text |
| Resource Type | Article |
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