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Jul 7, 2016

Scanner: Efficient Video Analysis at Scale GitHub tag Build Status

Scanner is a system for developing applications that efficiently process large video datasets.

To learn more about Scanner, see the documentation at, check out the various example applications, or read the SIGGRAPH 2018 Technical Paper: "Scanner: Efficient Video Analysis at Scale".


Scanner's documentation is hosted at Here are a few links to get you started:


If you'd like to contribute to the development of Scanner, you should first build Scanner from source.

Please submit a pull-request rebased against the most recent version of the master branch and we will review your changes to be merged. Thanks for contributing!

Running tests

You can run the full suite of tests by executing make test in the directory you used to build Scanner. This will run both the C++ tests and the end-to-end tests that verify the python API.


Scanner is an active research project, part of a collaboration between Stanford and Carnegie Mellon University. Please contact Alex Poms and Will Crichton with questions.

Scanner was developed with the support of the NSF (IIS-1539069), the Intel Corporation (through the Intel Science and Technology Center for Visual Cloud Computing and the NSF/Intel VEC program), and by Google.

Paper citation

Scanner was published at SIGGRAPH 2018 as "Scanner: Efficient Video Analysis at Scale" by Poms, Crichton, Hanrahan, and Fatahalian. If you use Scanner in your research, we'd appreciate it if you cite the paper with the following bibtex:

 author = {Poms, Alex and Crichton, Will and Hanrahan, Pat and Fatahalian, Kayvon},
 title = {Scanner: Efficient Video Analysis at Scale},
 journal = {ACM Trans. Graph.},
 issue_date = {August 2018},
 volume = {37},
 number = {4},
 month = jul,
 year = {2018},
 issn = {0730-0301},
 pages = {138:1--138:13},
 articleno = {138},
 numpages = {13},
 url = {},
 doi = {10.1145/3197517.3201394},
 acmid = {3201394},
 publisher = {ACM},
 address = {New York, NY, USA},