More about HKUST
Recognizing Human Activities from Physical and Virtual Worlds
The Hong Kong University of Science and Technology Department of Computer Science and Engineering PhD Thesis Defence Title: "Recognizing Human Activities from Physical and Virtual Worlds" By Mr. Hao HU Abstract Recognizing the activities underlying human actions has been an extensive research topic since early 1980s, where researchers usually focus on understanding the activities of human in the physical world. Recently, with the advent of OSNs (online social networks), more and more online social activities also start to emerge. In this proposal, we aim to provide solutions for recognizing human activities both in the physical world and in the virtual world. We start by surveying related works in these two areas and then study some specific challenges which are important to deploy these activity recognition systems in the real world. In this thesis, we investigate a number of specific problems we need to tackle when deploying activity recognition techniques in the real-world. We first analyze how to recognize multiple activities in the physical world environment, especially when such activities have concurrent and interleaving relationships. Next, we extend such a framework into the virtual world, by exploiting the relatedness of search queries to activities with interleaving relationships and propose a context-aware query classification algorithm. Secondly, we study the problem of recognizing abnormal activities. These abnormal activities are rare to happen and it is difficult to collect enough training data about them. We develop an algorithm based on the Hierarchical Dirichlet Process and the one-class Support Vector Machine to recognize abnormal activities when the training data is scarce. Finally, when we need to deploy the activity recognition systems in the real-world, it is impractical for us to collect enough training data for different activity recognition scenarios, especially when we need to collect training data for different persons and even for different actions. To solve this problem, we've developed a transfer learning-based activity recognition framework which borrows useful information from previously collected and learned activity recognition domains and then re-use such information into the new target activity recognition domain. Furthermore, we've conducted extensive experiments to demonstrate the effectiveness of our proposed approaches on real-world datasets collected from smart homes or sensor environments. We've also shown that our context-aware query classification algorithm could outperform state-of-the-art query classification approaches on real-world query engine search logs. At the end of this thesis, we discuss some possible directions and problems for future work and extensions. Date: Monday, 26 November 2012 Time: 10:00am - 12:00noon Venue: Room 3402 Lifts 17/18 Chairman: Prof. Yu-Hsing Wang (CIVL) Committee Members: Prof. Qiang Yang (Supervisor) Prof. Raymond Wong Prof. Dit-Yan Yeung Prof. Ke Yi Prof. Rong Zheng (ISOM) Prof. Xing Xie (Microsoft Research Asia, China) **** ALL are Welcome ****