Depth Driven People Counting Using Deep Region Proposal Network
文献类型：会议论文
| 作者 | Diping Song; Yu Qiao; Alessandro Corbetta |
| 出版日期 | 2017 |
| 会议地点 | 中国 |
| 英文摘要 | Abstract - People counting is a crucial subject in video surveillance application. Factors such as severe occlusions, scene perspective distortions in real application scenario make this task challenging. In this paper, we carefully designed a deep detection framework based on depth information for people counting in crowded environments. Our system performs head detection on depth images collected by an overhead vertical Kinect sensor. To the best of our knowledge, this is the first attempt to use the deep convolutional neural networks on depth images for people counting. We explored the impact of the number and quality of RPN positive anchors on the performance of Faster R-CNN and proposed a solution. Our method is very simple but effective, not only showing promising results but also efficiency as it runs in real-time at a frame rate of about 110 frames per second on a GPU |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/11765]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2017 |
| 推荐引用方式 GB/T 7714 | Diping Song,Yu Qiao,Alessandro Corbetta. Depth Driven People Counting Using Deep Region Proposal Network[C]. 见:. 中国. |
入库方式： OAI收割
来源：深圳先进技术研究院
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