Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
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<strong>Session</strong> WedCT1 <strong>Pegaso</strong> A <strong>Wednesday</strong>, <strong>October</strong> <strong>10</strong>, <strong>2012</strong>, 11:00–12:30<br />
Object Detection and Tracking<br />
Chair<br />
Co-Chair<br />
11:00–11:15 WedCT1.1<br />
Reliable Object Detection<br />
and Segmentation using Inpainting<br />
Ji Hoon Joung 1 , M. S. Ryoo 2<br />
Sunglok Choi 3 and Sung-Rak Kim 1<br />
1 Robotics Research Department, Hyundai Heavy Industries, South Korea<br />
2 Mobility and Robotic Systems Section, Jet Propulsion Laboratory, USA<br />
3 Robot Research Department, ETRI, South Korea<br />
• This paper presents a novel object detection and segmentation method<br />
utilizing an inpainting algorithm. We newly utilize inpainting to judge<br />
whether an object candidate region includes the foreground object or not.<br />
• The key idea is that if we erase a certain region from an image, the<br />
inpainting algorithm is expected to recover the erased image only when it<br />
belongs a background area.<br />
• We illustrate how our<br />
inpainting-based detection /<br />
segmentation approach<br />
benefits the object detection<br />
using two different pedestrian<br />
datasets<br />
Concept of the proposed method<br />
11:30–11:45 WedCT1.3<br />
Exploiting and modeling local 3D structure for<br />
predicting object locations<br />
Alper Aydemir and Patric Jensfelt<br />
Center for Autonomous Systems, KTH, Sweden<br />
• We propose the use of local 3D shape<br />
around objects in everyday scenes as a<br />
strong indicator of the placement of these<br />
objects. We call this the 3D context of an<br />
object.<br />
• We propose a conceptually simple and<br />
effective method to capture this<br />
information.<br />
• Our results show that 3D contextual<br />
information is a strong indicator of object<br />
placement in everyday scenes.<br />
• An RGB-D data set from five different<br />
countries in Europe was collected and<br />
used for evaluation.<br />
Top figure shows a kitchen<br />
scene, and the bottom figure the<br />
method’s response for the object<br />
cup.<br />
12:00–12:15 WedCT1.5<br />
Fast High Resolution 3D Laser Scanning<br />
by Real-Time Object Tracking and Segmentation<br />
Jens T. Thielemann, Asbjørn Berge, Øystein Skotheim<br />
and Trine Kirkhus<br />
SINTEF ICT, Norway<br />
E-mail: {jtt,trk,asbe,osk}@sintef.no<br />
• This paper presents a real-time contour tracking and object<br />
segmentation algorithm for 3D range images. The algorithm<br />
is used to control a novel micro-mirror based imaging laser<br />
scanner, which provides a dynamic trade-off between<br />
resolution and frame rate. The micro-mirrors are<br />
controllable, enabling us to speed up acquisition<br />
significantly by only sampling on the object that is tracked<br />
and of interest. As the hardware is under development, we<br />
benchmark our algorithms on data from a SICK LMS<strong>10</strong>0-<br />
<strong>10</strong>000 laser scanner mounted on a tilting platform. We find<br />
that objects are tracked and segmented well on pixel-level;<br />
that frame rate/resolution can be increased 3-4 times<br />
through our approach compared to scanners having static<br />
scan trajectories, and that the algorithm runs in 30<br />
ms/image on a Intel Core i7 CPU using a single core.<br />
11:15–11:30 WedCT1.2<br />
3D Textureless Object Detection and Tracking:<br />
An Edge-based Approach<br />
Changhyun Choi and Henrik I. Christensen<br />
College of Computing, Georgia Institute of Technology, USA<br />
Example frames from our detection and tracking results<br />
• An approach to textureless object detection and tracking of the 3D pose<br />
• Detection and tracking schemes are coherently integrated in a particle<br />
filtering framework on the SE(3)<br />
• For object detection, an efficient chamfer matching is employed<br />
• A set of coarse poses is estimated from the chamfer matching results<br />
• Particles are initialized from the coarse pose hypotheses by randomly<br />
drawing based on costs of the matching<br />
• To ensure the initialized particles are at or close to the global optimum, an<br />
annealing process is performed after the initialization<br />
• Comparative results for several image sequences with clutter are shown<br />
to validate the effectiveness of our approach<br />
11:45–12:00 WedCT1.4<br />
Birth Intensity Online Estimation in GM-PHD<br />
Filter for Multi-Target Visual Tracking<br />
Xiaolong Zhou, Y.F. Li, Tianxiang Bai and Yazhe Tang<br />
Dept. of MBE, City University of Hong Kong, Hong Kong, China<br />
Bingwei He<br />
Dept. of Mechanical Engineering and Automation, Fuzhou University, China<br />
• A multi-target visual tracking system that<br />
combines object detection and GM-PHD<br />
filter is developed<br />
• An improved measurement-dependent<br />
birth intensity online estimation method<br />
that based on the entropy distribution and<br />
the coverage rate is proposed<br />
• Entropy distribution based birth intensity<br />
update is proposed to remove those<br />
Gaussian components like noises within<br />
the birth intensity which are irrelevant with<br />
the birth measurements<br />
• Coverage rate based birth intensity update<br />
is proposed to further eliminate the noises<br />
<strong>2012</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–139–<br />
Tracking results comparison for<br />
data sets PETS 2000 and<br />
CAVIAR. (a) Detection results. (b)<br />
Tracking results with birth process<br />
proposed in [13]. (c) Tracking<br />
results with birth process proposed<br />
in this paper.<br />
12:15–12:30 WedCT1.6<br />
A Heteroscedastic Approach to Independent<br />
Motion Detection for Actuated Visual Sensors<br />
Carlo Ciliberto, Sean Ryan Fanello, Lorenzo Natale<br />
and Giorgio Metta<br />
Robotics Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Italy<br />
• A compositional framework is presented to<br />
perform real-time independent motion<br />
detection for applications in Robotics.<br />
• The algorithm can be easily adapted to a<br />
wide range of robotic platforms thanks to<br />
the flexibility granted by its modular<br />
structure.<br />
• The proposed method overcomes the<br />
shortcomings of the current state of the art<br />
approaches by exploiting the known robot<br />
kinematics to predict egomotion rather<br />
than relying on vision alone.<br />
• A heteroscedastic learning layer is<br />
employed to tune the egomotion predictive<br />
capabilities of the system.<br />
Experiments were conducted on<br />
the iCub humanoid robot.