Opencv

install opencv

 * opencv install script
 * https://gist.github.com/melvincabatuan/bcc21c9523b461e236fa889e556b2fe9 opencv install with cmake
 * https://gist.github.com/CorcovadoMing/ad7fde186287af261694

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

Neural nets
https://github.com/udacity/CarND-TensorFlow-L2 Udacity self driving car, open source.

Data sets
Neural network training is done on data sets of images.
 * http://commoncrawl.org/the-data/ entire internet archived, available for purchase.
 * https://www.reddit.com/r/dldata/, https://www.reddit.com/r/MachineLearning/ , https://www.reddit.com/r/datamining/
 * https://www.reddit.com/r/dldata/ links to https://graphics.ethz.ch/~perazzif/davis/code.html, https://github.com/fperazzi/davis DAVIS (Densely Annotated VIdeo Segmentation), consists of fifty high quality, Full HD video sequences, spanning multiple occurrences of common video object segmentation challenges such as occlusions, motion-blur and appearance changes. Each video is accompanied by densely annotated, pixel-accurate and per-frame ground truth segmentation. https://graphics.ethz.ch/%7Eperazzif/davis/index.html for youtube video links. http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Perazzi_A_Benchmark_Dataset_CVPR_2016_paper.pdf academic paper.
 * https://www.tensorflow.org/
 * https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html  https://research.googleblog.com/2016/09/introducing-open-images-dataset.html, https://github.com/openimages/dataset Today, we introduce Open Images, a dataset consisting of ~9 million URLs to images that have been annotated with labels spanning over 6000 categories. We tried to make the dataset as practical as possible: the labels cover more real-life entities than the 1000 ImageNet classes, there are enough images to train a deep neural network from scratch. https://research.googleblog.com/2015/07/deepdream-code-example-for-visualizing.html
 * http://megaface.cs.washington.edu/dataset/download.html 65gig data set of million faces, bounding boxes. See reddit/r/dldata
 * http://places.csail.mit.edu/demo.html This demo identifies if the image is an indoor or an outdoor place, and suggests the five most likely place categories representing the image, using Places-CNN (see project page). It is made for pictures of environments, places, views on a scene and a space (as opposed to picture of an object). You also could upload image using mobile phone. Upload .jpg or jpeg image only. The heatmap is generated by the CAM technique.
 * http://mi.eng.cam.ac.uk/projects/relocalisation/#dataset, https://github.com/alexgkendall/caffe-posenet POSENET Alex Kendall, Matthew Grimes and Roberto Cipolla "PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization." Proceedings of the International Conference on Computer Vision (ICCV), 2015.
 * The main goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit

counting cars
Chris Dahms, https://github.com/andrewssobral/vehicle_detection_haarcascades Andrew Sorbal

links

 * https://www.youtube.com/watch?v=QPgqfnKG_T4 militarizing backyard with python and machine vision talk(water jetting squirrels)
 * 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 , Caffe berkeley vision , OpenKcam , GPU ,
 * 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