The Smart Surveillance Framework is a C/C++ library built using the OpenCV and the C++ Standard Template Library to provide a set of functionalities to aid researchers not only on the development of surveillance systems but also on the creation of novel solutions for problems related to video surveillance.
One of its main goals is providing a set of data structures to describe the scene so that researchers are allowed to focus only on their problems of interest and use this information without creating such infrastructure to every problem that will be tackled, as it is done in the majority of case currently. For instance, if a researcher is working on individual action recognition, he/she would need to first capture the data, detect and track people, and only then recognize their actions. By using the SSF, one just needs to launch the detection and tracking modules to provide the people’s location. This allows one to concentrate only on the problem at hand, which is action recognition without worrying about how the data representation, storage and communication has to be designed.
The SSF was designed to provide features for a good scene understanding, scalability, real-time operation, multi-sensor environment, usage of low cost standard components, runtime re-configuration, and communication control.
The main benefits provided by the use of the SSF are the following:
- A framework to allow the processing of large amounts of data provided by multiple surveillance cameras;
- A platform to compare and exchange research results in which researchers can contribute with modules to solve specific problems;
- A framework to allow fast development of new video analysis techniques once one can focus only on his/her specific task;
- Creation of a high level semantic representation of the scene using data extracted by low level modules to allow activity recognition;
- A testbed to allow further development on activity understanding, since it is possible to focus on the activities that are using real data, instead of annotated data that may prevent the method from working on real environments.
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. If you download the SSF, you automatically agree with it. You may not use the SSF for commercial purposes.
You should cite the following papers if you use this software in your work.