Human activity detection in digital videos is currently attracting significant research interest. This problem is especially challenging for video datasets that have a lot of human activity, illumination noise, and structural noise. The video dataset associated with the Advancing Out of School Learning in Mathematics and Engineering (AOLME) project has these challenges. ALOME videos have been used in the study of human activities “in the wild”.
This thesis explores detection of hand movement using color and optical flow. Exploratory analysis considered the problem component wise on components created from thresholds applied to motion and color. The proposed approach uses patch color classification, space-time patches of video, and histogram of optical flow. The approach was validated on video patches extracted from 15 AOLME video clips. The approach achieved an average accuracy of 84% and an average receiver operating characteristic area under curve (ROC AUC) of 89%.
video analysis, activity detection, hand detection, skin, motion, movement
Level of Degree
Electrical and Computer Engineering
First Committee Member (Chair)
Second Committee Member
Third Committee Member
Darsey, Callie J.. "Hand Movement Detection in Collaborative Learning Environment Videos." (2018). https://digitalrepository.unm.edu/ece_etds/419