Advisor(s)
Octavia Camps
Contributor(s)
Jennifer Dy, Deniz Erdogmus
Date of Award
2011
Date Accepted
12-2011
Degree Grantor
Northeastern University
Degree Level
M.S.
Degree Name
Master of Science
Department or Academic Unit
College of Engineering, Department of Electrical and Computer Engineering
Keywords
computer engineering, artificial intelligence, robotics, surveillance
Disciplines
Electrical and Computer Engineering | Engineering
Abstract
In recent years, there has been an increasing interest within computer vision in the analysis of human activity for surveillance applications. These efforts are motivated by ubiquity of surveillance cameras and the need for security in large public spaces. The goal of human activity recognition from video is to classify an activity in a given video as one of several activities learned from training data. A related problem, event and anomaly detection, flags a behavior or event as abnormal when it deviates from previous available data. In this case, the activity is not known a priori. Instead, the goal is to look for something that has not been seen before.
In this thesis, we propose a new approach to exploit the temporal information embedded in video data to address problems in human activity analysis. The main idea is to model human behaviors as output of unknown dynamical systems while the initial conditions are unknown. We use Mixture of Gaussian to determine outliers, which are labeled as anomalies. We will introduce this approach in the context of activity recognition, event detection and anomaly detection.
Document Type
Master's Thesis
Rights Information
copyright 2011
Rights Holder
Teresa Yu Mao
Permanent URL
Recommended Citation
Mao, Teresa Yu, "Dynamics based approach for human activity understanding" (2011). Electrical and Computer Engineering Master's Theses. Paper 70. http://hdl.handle.net/2047/d20002112
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