Real time object tracking on Raspberry Pi 2 : A comparison between two tracking algorithms on Raspberry Pi 2

Sammanfattning: Object tracking has become a large field with a wide range of algorithms being used as a result. This thesis focuses on analyzing the performance in terms of successful tracking on moving objects using two popular tracking systems, Kanade-Lucas-Tomasi Feature Tracker and Camshift, on a single-board computer with live camera feed. The tracking is implemented in C++ with OpenCV on a Raspberry Pi 2 and the Raspberry Pi Camera Module as a camera feed.  The feature detector is chosen to good features to track[1] for KLT and Camshift uses color histogram for features. To be able to follow an object a pan and tilt system is built to be able to follow the object using tilting and panning motion. Two different objects, a tennis ball and book cover, are used in experiments to run the tests for performance of the different tracking systems. The system created is able to track a moving object and keeping it in the center of the image. The Camshift tracker performed the better in terms of successful tracking between the two algorithms in the experments made on the system.