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Showing posts from January, 2017

About Opencv 3.2 and Deep Regression Networks

Opencv 3.2, and Deep Regression Networks Opencv 3.2 is out. I am just checking the change log. What is inside from the my point of view ? The tuns of improvement are mainly in opencv_contrib modules Github fork is here . There is several thinks that should be mentioned. For example GOTURN tracker. Is also part of the opencv_contrib fork under the tracking modules.. Goturn is convolutional neural network based tracking algorithm. More information should be also found on  Learning to Track at 100 FPS with Deep Regression Networks . Deep Regression Networks (goturn basic information) In opposite to online learning (for example first version of TLD that using warp path of negative example and geometric transformation over positive sample) the proposed DRN (Deep regression networks) using somehow pretrained feed-forward network without online learning. The authors pre-train algorithms on many different video samples called generic object tracking. Let say there is a video

Amazon GO driven by modern computer vision from business perspective

Amazon GO driven by modern computer vision, sensor fusion and deep learning  In my eyes this is the brilliant idea. Just walk in, pick up what ever you want and leave. Get a milk or piece of bread and leave. All is automatically charged to your online account. What else ? You can comfortably put the staff back and go. You pay only what you take away. Great. What about the data collected behind. This is huge opportunity to optimize the process selling the product or even better the whole shopping cart  to concrete person. Wow effect is right there.  Data about customers  I would like to talk mainly about this topic.  In late 2013, I and my friends come with the idea about statistics from security cameras in retail stores. The goal is to recognize interest of your customers and optimize the offers, product placement and more based on the statistics taken from cameras. By comparison of the interest of the people and their behavior in segments (places) and compare to all othe

Head and people detection in opencv

LBP cascade for detect head and people in opencv  LBP cascade free to download to use in opencv to detect people and heads. Code example and cascade description. All you need to write your own people head detector from the youtube video. Cascade is trained on my own people and head datasets. There are no perfect but in some cases are better then default opencv cascades. They are just different.. For example you can count that the head detector have much more false detection than the people detector.. The shape and feature space is much more common and close to others shapes than the whole people detector. Issues with opencv detectMultiScale head and people detector Please let me know if cascades worked as expected.. In code example there is ground threshold settings and reccomentation.  LBP cascade head detection properties Sure you can find inside file. <!-- This is just basic 16 stage lbp cascade head detector develop by  V.K. from https:

Opencv Kalman filter example video head tracking. Code soon

Opencv Kalman filter example video head tracking Example of kalman filter in Opencv with head detection and tracking.  Two big tutorials will be published soon.  New version of LBP cascades for people detection, head detection Code and tutorial related to this example. Simple kalman filter for tracking in Opencv. I just finished the code for you.. Stay tuned and share :). Thanks