A Semi Automated Annotation System for Finding the Noteworthy in Video

Video annotation is the allocation of video shots or video segments to different, predefined semantic concepts such as person, car, sky, people walking, etc. We aim to present a  novel approach to semi-automatically annotate the videos based on visual attention, which can detect the focus of interest such as salient objects in video frames automatically. The pre-selection of regions of interest facilitates the recognition of objects of different shapes, poses, scales, and illuminations and benefits the tedious manual labeling. The advantages of both visual attention analysis and spatiotemporal processing will benefit multimedia signal processing industry in a promising application scenario of video summarizating using semantic annotations bases on a computer model of human attention.

Intern: 
Yaqing Niu
Superviseur universitaire: 
Dr. Adam Keith Anderson
Project Year: 
2014
Province: 
Ontario
Discipline: 
Programme: