Knowledge Extraction in Video Through the Interaction Analysis of Activities Knowledge Extraction in Video Through the Interaction Analysis of Activities

• Main Author: Florez, Omar Ulises
• Format: Others
• Published: DigitalCommons@USU 2013
• Subjects: Advanced Algorithms; Data Mining; Machine Learning; Multimedia Retrieval; Computer Sciences; Physical Sciences and Mathematics
• Online Access: https://digitalcommons.usu.edu/etd/1720; https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=2717&context=etd

Video is a massive amount of data that contains complex interactions between moving objects. The extraction of knowledge from this type of information creates a demand for video analytics systems that uncover statistical relationships between activities and learn the correspondence between content and labels. However, those are open research problems that have high complexity when multiple actors simultaneously perform activities, videos contain noise, and streaming scenarios are considered. The techniques introduced in this dissertation provide a basis for analyzing video. The primary contributions of this research consist of providing new algorithms for the efficient search of activities in video, scene understanding based on interactions between activities, and the predicting of labels for new scenes.