Engineering Commons^™

Long-Term Human Video Activity Quantification In Collaborative Learning Environments, Venkatesh Jatla

Electrical and Computer Engineering ETDs

Research on video activity detection has mainly focused on identifying well-defined human activities in short video segments, often requiring large-parameter systems and extensive training datasets. This dissertation introduces a low-parameter, modular system with rapid inference capabilities, capable of being trained on limited datasets without transfer learning from large-parameter systems. The system accurately detects specific activities and associates them with students in real-life classroom videos. Additionally, an interactive web-based application is developed to visualize human activity maps over long classroom videos.

Long-term video activity detection in classrooms presents challenges, such as multiple simultaneous activities, rapid transitions, long-term occlusions, duration exceeding 15 …

Fast Video-Based Face Recognition In Collaborative Learning Environments, Phuong Tran

Electrical and Computer Engineering ETDs

Face recognition is a classical problem in Computer Vision that has experienced significant progress recently. Yet, face recognition in videos remains challenging. In digital videos, face recognition is complicated by occlusion, pose and lighting variations, and persons entering and leaving the scene. The goal of the thesis is to develop a fast method for face recognition in digital videos that is applicable to large datasets. Instead of the standard video-based methods that are tested on short videos, the goal of the approach is to be applicable to long educational videos of several minutes to hours, with the ultimate goal of …

Hand Movement Detection In Collaborative Learning Environment Videos, Callie J. Darsey

Electrical and Computer Engineering ETDs

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 …