Monday, June 28, 2010

Lab Meeting June 29th, 2010 (KuoHuel): People Tracking with Human Motion Predictions from Social Forces (ICRA'10)

Title: People Tracking with Human Motion Predictions from Social Forces

Authors: Matthias Luber, Johannes A. Stork, Gian Diego Tipaldi, and Kai O. Arras

For many tasks in populated environments, robots need to keep track of present and future motion states of people. Most approaches to people tracking make weak assumptions on human motion such as constant velocity and direction. But even over a short period, human motion behavior is more complex and influenced by factors such as an intended goal, other people, objects in the environment, or social rules. Therefore, more sophisticated motion models are highly desirable especially since people frequently undergo lengthy occlusion events.
For the study of crowd behavior or evacuation dynamics, computational models that describe individual and collective pedestrian dynamics have been developed in e.g. the social psychology community. In this paper, we make use of such a model for the purpose of people tracking. Concretely, we integrate a pedestrian dynamics model based on social forces into a multi-hypothesis target tracker. We show how the re ned motion predictions translate into more informed probability distributions over hypotheses and nally into a more robust tracking behavior and better occlusion handling. In experiments in indoor and outdoor environments with data from a laser range nder, the social force model leads to more accurate tracking with up to two times fewer data association errors.

Lab Meeting June 29th, 2010 (Jeff): Fully Autonomous Trajectory Estimation with Long-Range Passive RFID

Title: Fully Autonomous Trajectory Estimation with Long-Range Passive RFID

Authors: Philipp Vorst and Andreas Zell


We present a novel approach which enables a mobile robot to estimate its trajectory in an unknown environment with long-range passive radio-frequency identi cation
(RFID). The estimation is based only on odometry and RFID measurements. The technique requires no prior observation model and makes no assumptions on the RFID setup. In
particular, it is adaptive to the power level, the way the RFID antennas are mounted on the robot, and environmental characteristics, which have major impact on long-range RFID
measurements. Tag positions need not be known in advance, and only the arbitrary, given infrastructure of RFID tags in the environment is utilized. By a series of experiments with a
mobile robot, we show that trajectory estimation is achieved accurately and robustly.

IEEE International Conference on Robotics and Automation(ICRA), May 2010

Monday, June 07, 2010

Lab Meeting June 8th, 2010 (Wang Li): Pictorial Structures for Object Recognition

Pictorial Structures for Object Recognition

Pedro F. Felzenszwalb
Daniel P. Huttenlocher
IJCV 61(1), 2005

In this paper we present a computationally efficient framework for part-based modeling and recognition of objects, motivated by the pictorial structure models introduced by Fischler and Elschlager. We address the problem of using pictorial structure models to find instances of an object in an image as well as the problem of learning an object model from training examples, presenting efficient algorithms in both cases. We demonstrate the techniques
by learning models that represent faces and human bodies and using the resulting
models to locate the corresponding objects in novel images.

Paper Link

Sunday, June 06, 2010