Real-time face detection and tracking
By Alice Namutebi and Haval Kadhem
This page contains information about a final project from the Embedded and Distributed AI course of the Spring 2020 semester. The focus has been developing real-time intelligent algorithms which can run on embedded systems.
This project looks at a problem of real-time object detection and tracking. The proposed framework exploited different algorithms for object detection and tracking, such as SIFT (Scale Invariant Feature Transform), SURF (Speeded-Up Robust Features) and ORB (Oriented FAST and Rotated BRIEF) and finally Cascade HAAR. The feature matching is accomplished by the Brute-Force, and FLANN (Fast Library for Approximate Nearest Neighbours) combined with the k-Nearest Neighbours algorithm. The proposed algorithms are designed and implemented using OpenCV library and example code from its online documentation. The various algorithms’ effectiveness is verified through analyses of their execution speed and number of frames they detect a person in a region of interest within a video segment.
For more information, you can contact to Alice and Haval: