Seminar
Visual Analysis of Dense Crowds
Event Location: Newell Simon Hall 1507Bio: Haroon Idrees is a postdoctoral researcher in the Center for Research in Computer Vision (CRCV) at the University of Central Florida (UCF). He is interested in machine vision and learning, with focus on crowd analysis, action recognition, multi-camera and airborne surveillance, as well as deep learning and multimedia content [...]
Haroon Idrees: Visual Analysis of Dense Crowds
Haroon Idrees Post Doc Associate, Center for Research in Computer Vision, University of Central Florida (UCF) Abstract Automated analysis of dense crowds is a challenging problem with far-reaching applications in crowd safety and management, as well as gauging political significance of protests and demonstrations. In this talk, I will first describe a counting approach which [...]
Toward Deep Geometric Image Understanding
Event Location: Newell Simon Hall 1507Bio: Jia Deng is an Assistant Professor of Computer Science and Engineering at the University of Michigan. His research focus is on computer vision and machine learning, in particular, achieving human-level visual understanding by integrating perception, cognition, and learning. He received his Ph.D. from Princeton University and his B.Eng. from [...]
Robots for the social good: Identifying and addressing organizational and societal factors in the design and use of robots
Event Location: NSH 1305Bio: I am an Associate Professor of Informatics and Cognitive Science at Indiana University, Bloomington, where I founded and direct the R-House Human-Robot Interaction Lab. My work combines the social studies of computing, focusing particularly on the design, use, and consequences of socially interactive and assistive robots in different social and cultural [...]
Selma Sabanovic: Robots for the social good: Identifying and addressing organizational and societal factors in the design and use of robots
Selma Sabanovic Associate Professor of Informatics and Cognitive Science, Indiana University Bloomington Additional Information Host: Aaron Steinfeld Appointments: Stephanie Matvey Abstract Robots are expected to become ubiquitous in the near future, working alongside and with people in everyday environments to provide various societal benefits. In contrast to this broad ranging social vision for robotics applications, [...]
A Fast & Efficient Mission Planner for Multi-rotor Aerial Vehicles in Large, High-resolution Maps of Cluttered Environments
Abstract: Unmanned aerial vehicles have many potential applications, such as monitoring crops and inspecting infrastructure. The potential benefits are greater if the UAV is semi- or fully-autonomous, requiring only occasional human oversight or none at all. This would allow the above use cases to be performed at lower cost, during any time of day, or [...]
Interactive Scene Understanding
Abstract Despite recent progress, AI is still far from understanding the physics of the world, and there is a large gap between the abilities of humans and the state-of-the-art AI methods. In this talk, I will focus on physics-based scene understanding and interactive visual reasoning, which are crucial next steps in computer vision and AI. [...]
Robotic Manipulation under clutter and uncertainty with and around people
Abstract Robots manipulate with super-human speed and dexterity on factory floors. But yet they fail even under moderate amounts of clutter or uncertainty. However, human teleoperators perform remarkable acts of manipulation with the same hardware. My research goal is to bridge the gap between what robotic manipulators can do now and what they are capable [...]
Mutual Information for Robust Visual Odometry
Abstract Off-the-shelf digital camera sensors often have limited dynamic range and real-world dynamic lighting changes adversely impact visual state estimation algorithms. This is because most conventional visual state estimation algorithms make the constancy of brightness assumption, wherein the intensity of a pixel is expected to be constant across small motions of a camera. However, this [...]