Opencv

Code

 * https://gist.github.com/moshekaplan/5106221 match two images.
 * http://soc.ninja/downloads/

install opencv

 * opencv install script

blas and lapack
Blas and lapack

Itseez
https://github.com/Itseez/gtc-2015-lab Jetson tegra cpu

https://github.com/Itseez/opencv_for_ios_book_samples opencv book coding examples solution

http://elinux.org/Main_Page  embedded linux and boards supported

http://roboticssamy.blogspot.com robot balancing

v4l

 * sudo apt-get install v4l-utils
 * http://www.techytalk.info/webcam-settings-control-ubuntu-fedora-linux-operating-system-cli/

Authors

 * Derek Molloy beaglebone code v4l-tcl.c file scripting usb camera, mpeg4 compression etc.
 * pyimagesearch.com
 * https://github.com/tesseract-ocr/tesseract/wiki OCR

Yuki nagai
https://www.youtube.com/watch?v=pj-QuE6pdEQ The red bounding box is "Boosting" result, green is "MIL", blue is "TLD", black is "Medianflow", and pink is "KCF". Tracking code: Dataset and evaluation code: Evaluation results:
 * http://cvlab.hanyang.ac.kr/tracker_benchmark/
 * https://arxiv.org/abs/1404.7584 High-Speed Tracking with Kernelized Correlation Filters
 * https://github.com/yuukicammy/opencv_tracker_performance_test/blob/master/opencv_tracker/dev/src/main_opencv_trackeing.cpp
 * https://sites.google.com/site/trackerbenchmark/benchmarks/v10
 * https://github.com/yuukicammy/opencv_tracker_performance_test/tree/master/tracker_benchmark_v1.0/figs/overall/OpenCV
 * https://github.com/yuukicammy/struck  Struck: Structured Output Tracking with Kernels http://www.samhare.net/research/struck
 * http://eigen.tuxfamily.org

papers
https://www.researchgate.net/publication/279057771_Expert_Video-Surveillance_System_for_Real-Time_Detection_of_Suspicious_Behaviors_in_Shopping_Malls

http://www4.comp.polyu.edu.hk/~cslzhang/CT/CT.htm Real-time Compressive Tracking and c++ code

forum questions
http://stackoverflow.com/questions/28619037/opencv-where-is-tracking-hpp itzees repository

http://stackoverflow.com/questions/36254452/counting-cars-opencv-python-issue?rq=1 python counting cars by http://stackoverflow.com/users/3962537/dan-ma%c5%a1ek

Udacity
Udacity

Data sets
Neural data sets Neural network training is done on data sets of images.,
 * http://www.ibm.com/watson/developercloud/visual-recognition.html, https://visual-recognition-demo.mybluemix.net/ , https://github.com/watson-developer-cloud/visual-recognition-nodejs  The Visual Recognition Service uses deep learning algorithms to analyze images for scenes, objects, faces, text, and other subjects that can give you insights into your visual content. You can organize image libraries, understand an individual image, and create custom classifiers for specific results that are tailored to your needs.
 * http://cloudsightapi.com/
 * http://cloudcv.org/ and python cloud API github We are witnessing a proliferation of massive visual data. Unfortunately scaling existing computer vision algorithms to large datasets leaves researchers repeatedly solving the same algorithmic, logistical, and infrastructural problems. Our goal is to democratize computer vision; one should not have to be a computer vision, big data and distributed computing expert to have access to state-of-the-art distributed computer vision algorithms. We present CloudCV, a comprehensive system to provide access to state-of-the-art distributed computer vision algorithms as a cloud service through a Web Interface and APIs. Uses https://turi.com/products/create/open_source.html commercial machine learning algorithms.
 * http://image-net.org/challenges/LSVRC/2015/index

counting cars
Chris Dahms, https://github.com/andrewssobral/vehicle_detection_haarcascades Andrew Sorbal
 * https://github.com/openalpr/openalpr 997 forks ,http://www.openalpr.com/ license plate recognition.

Leenissen

 * http://leenissen.dk/fann/wp/,  https://github.com/Counterfeiter/Q-LearningRobot
 * https://hackaday.com/2016/11/02/machine-learning-foundations/
 * https://hackaday.com/2016/11/08/perceptrons-in-c/

links

 * https://www.oreilly.com/ideas/how-to-build-an-autonomous-voice-controlled-face-recognizing-drone-for-200, https://www.microsoft.com/cognitive-services/en-us/apis    Microsoft, Google, IBM, and Amazon all have fast, inexpensive cloud machine learning APIs. In the end, I used Microsoft’s Cognitive Service APIs for this project because it’s the only API that offers custom facial recognition
 * https://www.youtube.com/watch?v=QPgqfnKG_T4 militarizing backyard with python and machine vision talk(water jetting squirrels). Something a bit more effective on intruders see Net gun, http://sasecurity.wikia.com/wiki/Category:Guns
 * https://pythonprogramming.net/search/?q=opencv python tutorial on opencv by sentdex youtube
 * OpenTLD cppmta preferred over opentld
 * simplecv vision framework for vision applications, acccess OpenCV  without having to first learn about bit depths, file formats, color spaces, buffer management, eigenvalues, or matrix versus bitmap storage. This is computer vision made easy.
 * https://ukoethe.github.io/vigra/ Vision with Generic Algorithms library
 * Ffmpeg, Libccv, Segnet clone of caffe detects pets, people,cars
 * Caffe berkeley vision, OpenKcam ,
 * GPU Nvidia Tegra jetson APU(cpu/gpu) platform for embedded vision, robotics.
 * Dlib library C++ vision library like caffe and opencv
 * KLT vision, Slam lsd slam 2013 journal paper with github code
 * Embedded vision alliance
 * https://groups.google.com/forum/?hl=en#!forum/visual-tracking Google image groups.
 * https://www.researchgate.net/post/Which_is_the_best_tracking_algorithm_available which algorithm to use.
 * http://deeplearning.net/software/theano/introduction.html Theano gpu
 * Thine image firm