Kernelized Correlation Filters (KCF) with pan-tilt object tracking скачать в хорошем качестве

Kernelized Correlation Filters (KCF) with pan-tilt object tracking

A Python OpenCV and Python Lego NXT implementation of an object tracking webcam that is mounted on a Lego pan-tilt device. Using OpenCV 3.4.0 with a KCF (Kernelized Correlation Filters) tracker. More information here https://sites.google.com/site/eyalabr.... The program requires a connected webcam and an NXT pan-tilt mechanical setup, and has three operating modes: 'm' key Mark: enter this mode while holding an object in front of the webcam. Use the mouse to mark the region that you want to track. 's' key Show: this is a test mode that shows the tracked object. Use this mode to test tracking on the display. 't' key Track: this mode activates the motors and sends power controls to them. The Track mode will be halted (red rectangle) if the object is lost. Tracking will resume automatically when the is re-detected. Mechanical setup Several options existed for the Pan-Tilt mechanical setup. There is a nice Differential Pan & Tilt: [   • Differential-drive pan and tilt mecha...  ] that I built first, but, being made out of Lego, it proved not to be robust enough. The backlash from the motors and the gears, especially the horizontal one, was excessive. The vertical axis was too 'wobbly' to hold a webcam. I opted for this setup [   • Object tracking camera  ]. The tilt movement when panning was not an issue because the tilt PID took care of any error. The mechanical arrangement was much 'tighter' and backlash was minimal due to better gearing. There are many other arrangements to be found, but these were the most elegant ones I found with Lego. The PID is not perfectly tunes, you will notice some overshoot.