Monday, April 15, 2013 - 10:00am to 11:00am
KEC 1007

Speaker Information

Raffay Hamid
Staff Research Scientist
eBay Research Labs


To make computers proactive and assistive, we must enable them to perceive, learn, and predict what is happening in their surroundings. A key challenge to this end is to bridge the gap between the low-level perceptual inputs and the semantically useful inferences. A natural way to approach this challenge is to have a set of intermediate characterizations that could appropriately channel low-level information such that it could be used to draw useful high-level inference. In this talk, I will address some of the challenges associated with one such set of intermediate characterizations, including (i) key-objects present in an environment, (ii) interactions among these key-objects that define various actions, and (iii) sequences of different actions that compose various activities.

I will begin by focusing on the problem of localization and tracking of key-objects in an environment, especially when they are being observed using multiple cameras. I will present a method to model the problem of fusing information from multiple cameras as finding cycles in complete K-partite graphs, and will summarize a class of greedy algorithms that can search for these cycles in an efficient manner. Using sports visualization as a motivating application, I will present results of our work on close to 300,000 frames of real soccer footage captured over a diverse set of playing conditions.

Next, I will talk about the problem of accurate detection of different actions performed in an environment. Exploiting the perceptual similarity that naturally exists among multiple actions, I will present a method of adaptively sharing information among multiple actions in order to simultaneously learn their discriminative models. I will present results of our learning framework on a set of 10 different actions performed in real soccer games.

Finally, I will focus on the problem of learning structure of activities performed in everyday environments. I will particularly talk about the representation of n-grams that attempt to learn the global structure of activities by using their local action-statistics. I will discuss how such a data-driven approach towards activity modeling can help discover and characterize human activities, and learn typical behaviors crucial for detecting irregular occurrences in an environment.

Speaker Bio

Raffay Hamid Staff Research Scientist at eBay Research Labs. Before joining eBay Research, Raffay was a post-doctoral research associate at Disney Research Pittsburgh, in conjunction with Carnegie Mellon University. His research interests lie at the intersection of Computer Vision, Statistical Learning, and Ubiquitous Computing. His work has mostly focused on building computational systems that can learn models of everyday human activities.

Raffay completed his PhD in 2008, from the College of Computing, at Georgia Institute of Technology. He has worked as a research intern at Intel, Mitsubishi Electronic, and Microsoft Research. Prior to graduate school, Raffay was at Techlogix Inc. collaborating with General Motors to develop a sensor-based intelligent system for automobiles. He has served as an adjunct lecturer at the University of Engineering and Technology, Lahore Pakistan, from 2001 to 2002. He was awarded the National Merit Scholarship from the Government of Pakistan from 1994 to 2001. More information about his interests can be found at: