  Human activity detection from digital videos presents many challenges to the
computer vision and image processing communities. Recently, many methods have
been developed to detect human activities with varying degree of success. Yet,
the general human activity detection problem remains very challenging,
especially when the methods need to work 'in the wild' (e.g., without having
precise control over the imaging geometry). The thesis explores phase-based
solutions for (i) detecting faces, (ii) back of the heads, (iii) joint
detection of faces and back of the heads, and (iv) whether the head is looking
to the left or the right, using standard video cameras without any control on
the imaging geometry. The proposed phase-based approach is based on the
development of simple and robust methods that rely on the use of Amplitude
Modulation- Frequency Modulation (AM-FM) models. The approach is validated
using video frames extracted from the Advancing Out-of-school Learning in
Mathematics and Engineering (AOLME) project. The dataset consisted of 13,265
images from ten students looking at the camera, and 6,122 images from five
students looking away from the camera. For the students facing the camera, the
method was able to correctly classify 97.1% of them looking to the left and
95.9% of them looking to the right. For the students facing the back of the
camera, the method was able to correctly classify 87.6% of them looking to the
left and 93.3% of them looking to the right. The results indicate that AM-FM
based methods hold great promise for analyzing human activity videos.
