Fusing Crowd Density Maps and Visual Object Trackers for People Tracking in Crowd Scenes
文献类型：会议论文
| 作者 | Kang, Di2; Ren WH(任卫红)1,2,3 ; Chan, Antoni B.2; Tang YD(唐延东)3
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| 出版日期 | 2018 |
| 会议日期 | June 18-22, 2018 |
| 会议地点 | Salt Lake City, USA |
| 页码 | 5353-5362 |
| 英文摘要 | While visual tracking has been greatly improved over the recent years, crowd scenes remain particularly challenging for people tracking due to heavy occlusions, high crowd density, and significant appearance variation. To address these challenges, we first design a Sparse Kernelized Correlation Filter (S-KCF) to suppress target response variations caused by occlusions and illumination changes, and spurious responses due to similar distractor objects. We then propose a people tracking framework that fuses the SKCF response map with an estimated crowd density map using a convolutional neural network (CNN), yielding a refined response map. To train the fusion CNN, we propose a two-stage strategy to gradually optimize the parameters. The first stage is to train a preliminary model in batch mode with image patches selected around the targets, and the second stage is to fine-tune the preliminary model using the real frame-by-frame tracking process. Our density fusion framework can significantly improves people tracking in crowd scenes, and can also be combined with other trackers to improve the tracking performance. We validate our framework on two crowd video datasets. |
| 产权排序 | 1 |
| 会议录 | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-5386-6420-9 |
| WOS记录号 | WOS:000457843605052 |
| 源URL | [http://ir.sia.cn/handle/173321/23851]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Ren WH(任卫红) |
| 作者单位 | 1.3University of Chinese Academy of Sciences 2.Department of Computer Science, City University of Hong Kong 3.2State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Kang, Di,Ren WH,Chan, Antoni B.,et al. Fusing Crowd Density Maps and Visual Object Trackers for People Tracking in Crowd Scenes[C]. 见:. Salt Lake City, USA. June 18-22, 2018. |
入库方式： OAI收割
来源：沈阳自动化研究所
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