Date of Award

2020

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

Committee Chair

Ramazan Aygun

Committee Member

Haeyong Chung

Committee Member

Huaming Zhang

Subject(s)

Image processing--Digital techniques., Image segmentation., Pattern recognition systems.

Abstract

Finding periodic segments in videos has a wide range of applications like recognizing and classifying actions in a video. In this thesis, we present a solution to the problem of identifying repetitive segments in a video and finding the number of periodic actions appearing in these repetitive segments in an unsupervised manner. The proposed method generates time-series data from the distance matrix of frames in a video. The time-series data is then analyzed to first determine the intervals where repetitions occur and then compute the number of periodic actions in these segments. Our method was evaluated using the MHAD202-v dataset. The experimental results show that the repetitive actions were detected with precision around 0.95 and an F1-score greater than 0.91. The error rate for the count of periodic segments was just around 15.8%.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.