Dlib library

http://dlib.net/ Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charge.

git clone https://github.com/davisking/dlib, iandees gist.github As of this writing, dlib won't compile due to weirdness with the system-installed libjpeg, so the developer suggests modifying line 277 of dlib/CMakeLists.txt to look like this: if (JPEG_FOUND AND LIBJPEG_IS_GOOD AND NOT APPLE)

http://blog.dlib.net/2016/10/easily-create-high-quality-object.html Convolutoin neural network CNN face detection.

http://blog.dlib.net/2016/06/a-clean-c11-deep-learning-api.html elastic net and quadratic program solvers. But the feature I'm most excited about is the new deep learning API. There are a lot of existing deep learning frameworks, but none of them have clean C++ APIs. You have to use them through a language like Python or Lua, which is fine in and of itself. But if you are a professional software engineer working on embedded computer vision projects you are probably working in C++, and using those tools in these kinds of applications can be frustrating. So if you use C++ to do computer vision work then dlib's deep learning framework is for you. It makes heavy use of C++11 features, allowing it to expose a very clean and lightweight API. For example, the venerable Lenet can be defined in pure C++

http://blog.dlib.net/2014/02/dlib-186-released-make-your-own-object.html HOG algorithm vs haar from opencv https://www.youtube.com/watch?v=LsK0hzcEyHI

https://github.com/tzutalin/dlib-android-app android fork, person and face detection.

github
https://github.com/bikz05/object-tracker Once the code starts, it will start the video file or the live stream. To select the objects to be tracked, pause the video by pressing the p key.The next step is to create a bounding box around the object(s) to be tracked. Press the mouse to select the top-left pixel location of the object to be tracked and then release the mouse on the bottom-right location of the object to be tracked.

links

 * Opencv, Caffe berkeley vision C++ vision, Libccv, machine learning, neural network libraries.
 * Segnet Clone of Caffe, detects people,cars, buildings ,pets.
 * Slam