RGB-W Dataset

<strong>Abstract</strong> Inspired by the recent success of RGB-D cameras, we propose the enrichment of RGB data with an additional quasi-free modality, namely, the wireless signal emitted by individuals' cell phones, referred to as RGB-W. The received signal strength acts as a rough proxy for depth and a reliable cue on a person's identity. Although the measured signals are noisy, we demonstrate that the combination of visual and wireless data significantly improves the localization accuracy. We introduce a novel image-driven representation of wireless data which embeds all received signals onto a single image. We then evaluate the ability of this additional data to (i) locate persons within a sparsity-driven framework and to (ii) track individuals with a new confidence measure on the data association problem. Our solution outperforms existing localization methods. It can be applied to the millions of currently installed RGB cameras to better analyze human behavior and offer the next generation of high-accuracy location-based services. <strong>Conference Paper</strong> PDF: http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Alahi_RGB-W_When_Vision_ICCV_2015_paper.pdf <strong>Metadata</strong> <pre><code>+----------------+-----------------+-----------+-----------+--------------+----------+ | Sequence Name | Length (mm:ss) | # Frames | # People | # W Devices | Download | +----------------+-----------------+-----------+-----------+--------------+----------+ | conference-1 | 01:53 | 1,697 | 5 | 5 | 116 MiB | | conference-2 | 05:18 | 4,782 | 12 | 12 | 379 MiB | | conference-3 | 23:31 | 21,165 | 1 | 2 | 1.3 GiB | | conference-4 | 06:27 | 4,832 | 1 | 2 | 357 MiB | | conference-5 | 06:03 | 4,525 | 2 | 2 | 290 MiB | | patio-1 | 07:22 | 6,636 | 4 | 4 | 474 MiB | | patio-2 | 04:36 | 4,144 | 2 | 2 | 258 MiB | | Full Dataset | 55:10 | 47,781 | -- | -- | 3.2 GiB | +----------------+-----------------+-----------+-----------+--------------+----------+</code></pre> <strong>Citation</strong> If you would like to cite our work, please use the following. <strong>Alahi A, Haque A, Fei-Fei L. (2015). RGB-W: When Vision Meets Wireless. International Conference on Computer Vision (ICCV). Santiago, Chile. IEEE.</strong> <pre>@inproceedings{alahi2015rgb, title={RGB-W: When vision meets wireless}, author={Alahi, Alexandre and Haque, Albert and Fei-Fei, Li}, booktitle={International Conference on Computer Vision}, year={2015} }</pre>## 摘要 受近期RGB-D相机（RGB-D camera）技术产业化落地的启发，我们提出为RGB数据新增一种近乎零成本的传感模态——即由个人手机发射的无线信号，我们将该数据集命名为RGB-W（RGB-W）。接收到的信号强度可作为深度信息的粗略替代指标，同时也是识别个体身份的可靠依据。尽管实测信号存在噪声，但我们的实验证明，视觉数据与无线数据的融合可显著提升定位精度。我们提出了一种全新的无线数据图像化表征方式，可将所有接收到的无线信号整合至单幅图像中。随后我们评估了该新增数据的两项应用能力：(1) 在稀疏驱动框架下实现人员定位；(2) 针对数据关联问题引入全新置信度度量方法以完成个体追踪。我们的方案性能优于现有定位方法，可部署于当前已批量部署的数百万台RGB相机中，用于更精准地分析人类行为，并提供新一代高精度基于位置的服务。 ## 会议论文 PDF下载链接：http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Alahi_RGB-W_When_Vision_ICCV_2015_paper.pdf ## 元数据 +----------------+-----------------+-----------+-----------+--------------+----------+ | 序列名称 | 时长(分:秒) | 帧数 | 人员数量 | 无线设备数量 | 下载大小 | +----------------+-----------------+-----------+-----------+--------------+----------+ | conference-1 | 01:53 | 1,697 | 5 | 5 | 116 MiB | | conference-2 | 05:18 | 4,782 | 12 | 12 | 379 MiB | | conference-3 | 23:31 | 21,165 | 1 | 2 | 1.3 GiB | | conference-4 | 06:27 | 4,832 | 1 | 2 | 357 MiB | | conference-5 | 06:03 | 4,525 | 2 | 2 | 290 MiB | | patio-1 | 07:22 | 6,636 | 4 | 4 | 474 MiB | | patio-2 | 04:36 | 4,144 | 2 | 2 | 258 MiB | | 完整数据集 | 55:10 | 47,781 | -- | -- | 3.2 GiB | +----------------+-----------------+-----------+-----------+--------------+----------+ ## 引用 若需引用本研究，请使用如下格式： > Alahi A, Haque A, Fei-Fei L. (2015). RGB-W: When Vision Meets Wireless. 国际计算机视觉大会（International Conference on Computer Vision, ICCV）. 智利圣地亚哥, IEEE. bibtex @inproceedings{alahi2015rgb, title={RGB-W: When vision meets wireless}, author={Alahi, Alexandre and Haque, Albert and Fei-Fei, Li}, booktitle={International Conference on Computer Vision}, year={2015} }