License and CityCam Release Agreement

The goal of the CityCam database is to provide a versatile platform for a wide range of computer vision research topics related to traffic videos. Therefore, the CityCam database is now made available for research purpose only. CMU serves as the technical agent and reserves the ultimate interpretation right for distribution of the database.

This dataset is released under the Creative Commons Attributin 4.0 License and researcher(s) agrees to the following restrictions on the CityCam database:

1. The CityCam database is available for non-commercial research purposes only.
2. All images of the CityCam database are obtained from the Internet which are not property of CMU. CMU is not responsible for the content nor the meaning of these images.
3. You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
4. You agree not to further copy, publish or distribute any portion of the CityCam database. Except, for internal use at a single site within the same organization it is allowed to make copies of the database.
5. CMU reserves the right to terminate your access to the database at any time.
6. All submitted papers or any publicly available text using the CityCam database must cite the following paper:
Shanghang Zhang, Guanhang Wu, João P. Costeira, and José MF Moura. "Understanding Traffic Density from Large-Scale Web Camera Data." In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 4264-4273.
7. The ultimate explanation of this agreement refers to CMU.

Registration and Download

To download our full dataset please fill the form below. A download link will be sent to the email. By submitting this form researchers agree with our license and user agreement. All published material with CityCam data must cite article
Shanghang Zhang, Guanhang Wu, João P. Costeira, and José MF Moura, Understanding Traffic Density from Large-Scale Web Camera Data, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 4264-4273.