Lab Meeting March 30, 2011 (Chih-Chung): Progress Report

I will show my recent work of moving target tracking and following, using laser scanner and PIONEER3 robot.

Lab Meeting March 30, 2011 (Chung-Han): Progress Report

I will show the updated ground-truth annotation system with the newly collected data set.

Lab Meeting March 23, 2011 (David): Object detection and tracking for autonomous navigation in dynamic environments (IJRR 2010)

Title: Object detection and tracking for autonomous navigation in dynamic environments (IJRR 2010)

Authors: Andreas Ess, Konrad Schindler, Bastian Leibe, Luc Van Gool

Abstract:
We address the problem of vision-based navigation in busy inner-city locations, using a stereo rig mounted on a mobile platform. In this scenario semantic information becomes important: rather than modeling moving objects as arbitrary obstacles, they should be categorized and tracked in order to predict their future behavior. To this end, we combine classical geometric world mapping with object category detection and tracking. Object-category-specific detectors serve to find instances of the most important object classes (in our case pedestrians and cars). Based on these detections, multi-object tracking recovers the objects' trajectories, thereby making it possible to predict their future locations, and to employ dynamic path planning. The approach is evaluated on challenging, realistic video sequences recorded at busy inner-city locations.

Link

Lab Meeting March 23, 2011 (Shao-Chen): A Comparison of Track-to-Track Fusion Algorithms for Automotive Sensor Fusion (MFI2008)

Title: A Comparison of Track-to-Track Fusion Algorithms for Automotive Sensor Fusion (MFI2008, Multisensor Fusion and Integration for Intelligent Systems)

Authors: Stephan Matzka and Richard Altendorfer

Abstract:

In  exteroceptive  automotive  sensor  fusion,  sensor data are usually only available as processed, tracked object data and not as  raw sensor data. Applying a Kalman filter  to such data  leads  to  additional  delays  and  generally  underestimates the  fused  objects'  covariance  due  to temporal  correlations  of individual  sensor  data  as  well  as inter-sensor  correlations.  We compare the performance of a standard asynchronous Kalman filter applied to tracked sensor data to several algorithms for the track-to-track fusion  of sensor objects of unknown  correlation, namely covariance  union,  covariance  intersection,  and  use  of cross-covariance.  For  the  simulation  setup  used  in  this  paper, covariance  intersection  and  use  of  cross-covariance  turn  out to  yield  significantly  lower  errors  than  a  Kalman  filter  at  a comparable computational load.

Link

Lab Meeting March 16th, 2011 (Andi): 3D Deformable Face Tracking with a Commodity Depth Camera

Qin Cai , David Gallup , Cha Zhang and Zhengyou Zhang

Abstract: Recently, there has been an increasing number of depth cameras available at commodity prices. These cameras can usually capture both color and depth images in real-time, with limited resolution and accuracy. In this paper, we study the problem of 3D deformable face tracking with such commodity depth cameras. A regularized maximum

likelihood deformable model fitting (DMF) algorithm is developed, with special emphasis on handling the noisy input depth data. In particular, we present a maximum likelihood solution that can accommodate sensor noise represented by an arbitrary covariance matrix, which allows more elaborate modeling of the sensor’s accuracy. Furthermore, an 1 regularization scheme is proposed based on the semantics of the deformable face model, which is shown to be very effective in improving the tracking results. To track facial movement in subsequent frames, feature points in the texture images are matched across frames and integrated into the DMF framework seamlessly. The effectiveness of the proposed method is demonstrated with multiple sequences with ground truth information.

Full Paper

Lab Meeting March 9th, 2011(KuoHuei): progress report

I will present my progress on Neighboring Objects Interaction models and tracking system.

Lab Meeting March 9, 2011 (Wang Li): Real-time Identification and Localization of Body Parts from Depth Images (ICRA 2010)

Real-time Identification and Localization of Body Parts from Depth Images

Christian Plagemann
Varun Ganapathi
Daphne Koller
Sebastian Thrun

Abstract

We deal with the problem of detecting and identifying body parts in depth images at video frame rates. Our solution involves a novel interest point detector for mesh and range data that is particularly well suited for analyzing human shape. The interest points, which are based on identifying geodesic extrema on the surface mesh, coincide with salient points of the body, which can be classified using local shape descriptors. Our approach also provides a natural way of estimating a 3D orientation vector for a given interest point. This can be used to normalize the local shape descriptors to simplify the classification problem as well as to directly estimate the orientation of body parts in space.
Experiments show that our interest points in conjunction with a boosted patch classifier are significantly better in detecting body parts in depth images than state-of-the-art sliding-window based detectors.

Paper Link

Article: Perception beyond the Here and Now

by Albrecht Schmidt, Marc Langheinrich, and Kristian Kersting
Computer, February 2011, pp. 86–88

A multitude of senses provide us with information about the here and now. What we see, hear, and feel in turn shape how we perceive our surroundings and understand the world. Our senses are extremely limited, however, and ever since humans began creating and using technology, they have tried to enhance their natural perception in various ways. (pdf)