Deep learning of scene-specific classifier for pedestrian detection
文献类型：会议论文
| 作者 | Zeng Xingyu; Ouyang Wanli; Wang Meng; Wang Xiaogang |
| 出版日期 | 2014 |
| 会议名称 | 13th European Conference on Computer Vision, ECCV 2014 |
| 会议地点 | Zurich, Switzerland |
| 英文摘要 | The performance of a detector depends much on its training dataset and drops significantly when the detector is applied to a new scene due to the large variations between the source training dataset and the target scene. In order to bridge this appearance gap, we propose a deep model to automatically learn scene-specific features and visual patterns in static video surveillance without any manual labels from the target scene. It jointly learns a scene-specific classifier and the distribution of the target samples. Both tasks share multi-scale feature representations with both discriminative and representative power. We also propose a cluster layer in the deep model that utilizes the scene-specific visual patterns for pedestrian detection. Our specifically designed objective function not only incorporates the confidence scores of target training samples but also automatically weights the importance of source training samples by fitting the marginal distributions of target samples. It significantly improves the detection rates at 1 FPPI by 10% compared with the state-of-the-art domain adaptation methods on MIT Traffic Dataset and CUHK Square Dataset. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/6223]  |
| 专题 | 深圳先进技术研究院_其他 |
| 作者单位 | 2014 |
| 推荐引用方式 GB/T 7714 | Zeng Xingyu,Ouyang Wanli,Wang Meng,et al. Deep learning of scene-specific classifier for pedestrian detection[C]. 见:13th European Conference on Computer Vision, ECCV 2014. Zurich, Switzerland. |
入库方式： OAI收割
来源：深圳先进技术研究院
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