Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm

Session WedCT1 Pegaso A Wednesday, October 10, 2012, 11:00–12:30
Object Detection and Tracking
Chair
Co-Chair
11:00–11:15 WedCT1.1
Reliable Object Detection
and Segmentation using Inpainting
Ji Hoon Joung 1 , M. S. Ryoo 2
Sunglok Choi 3 and Sung-Rak Kim 1
1 Robotics Research Department, Hyundai Heavy Industries, South Korea
2 Mobility and Robotic Systems Section, Jet Propulsion Laboratory, USA
3 Robot Research Department, ETRI, South Korea
• This paper presents a novel object detection and segmentation method
utilizing an inpainting algorithm. We newly utilize inpainting to judge
whether an object candidate region includes the foreground object or not.
• The key idea is that if we erase a certain region from an image, the
inpainting algorithm is expected to recover the erased image only when it
belongs a background area.
• We illustrate how our
inpainting-based detection /
segmentation approach
benefits the object detection
using two different pedestrian
datasets
Concept of the proposed method
11:30–11:45 WedCT1.3
Exploiting and modeling local 3D structure for
predicting object locations
Alper Aydemir and Patric Jensfelt
Center for Autonomous Systems, KTH, Sweden
• We propose the use of local 3D shape
around objects in everyday scenes as a
strong indicator of the placement of these
objects. We call this the 3D context of an
object.
• We propose a conceptually simple and
effective method to capture this
information.
• Our results show that 3D contextual
information is a strong indicator of object
placement in everyday scenes.
• An RGB-D data set from five different
countries in Europe was collected and
used for evaluation.
Top figure shows a kitchen
scene, and the bottom figure the
method’s response for the object
cup.
12:00–12:15 WedCT1.5
Fast High Resolution 3D Laser Scanning
by Real-Time Object Tracking and Segmentation
Jens T. Thielemann, Asbjørn Berge, Øystein Skotheim
and Trine Kirkhus
SINTEF ICT, Norway
E-mail: {jtt,trk,asbe,osk}@sintef.no
• This paper presents a real-time contour tracking and object
segmentation algorithm for 3D range images. The algorithm
is used to control a novel micro-mirror based imaging laser
scanner, which provides a dynamic trade-off between
resolution and frame rate. The micro-mirrors are
controllable, enabling us to speed up acquisition
significantly by only sampling on the object that is tracked
and of interest. As the hardware is under development, we
benchmark our algorithms on data from a SICK LMS100-
10000 laser scanner mounted on a tilting platform. We find
that objects are tracked and segmented well on pixel-level;
that frame rate/resolution can be increased 3-4 times
through our approach compared to scanners having static
scan trajectories, and that the algorithm runs in 30
ms/image on a Intel Core i7 CPU using a single core.
11:15–11:30 WedCT1.2
3D Textureless Object Detection and Tracking:
An Edge-based Approach
Changhyun Choi and Henrik I. Christensen
College of Computing, Georgia Institute of Technology, USA
Example frames from our detection and tracking results
• An approach to textureless object detection and tracking of the 3D pose
• Detection and tracking schemes are coherently integrated in a particle
filtering framework on the SE(3)
• For object detection, an efficient chamfer matching is employed
• A set of coarse poses is estimated from the chamfer matching results
• Particles are initialized from the coarse pose hypotheses by randomly
drawing based on costs of the matching
• To ensure the initialized particles are at or close to the global optimum, an
annealing process is performed after the initialization
• Comparative results for several image sequences with clutter are shown
to validate the effectiveness of our approach
11:45–12:00 WedCT1.4
Birth Intensity Online Estimation in GM-PHD
Filter for Multi-Target Visual Tracking
Xiaolong Zhou, Y.F. Li, Tianxiang Bai and Yazhe Tang
Dept. of MBE, City University of Hong Kong, Hong Kong, China
Bingwei He
Dept. of Mechanical Engineering and Automation, Fuzhou University, China
• A multi-target visual tracking system that
combines object detection and GM-PHD
filter is developed
• An improved measurement-dependent
birth intensity online estimation method
that based on the entropy distribution and
the coverage rate is proposed
• Entropy distribution based birth intensity
update is proposed to remove those
Gaussian components like noises within
the birth intensity which are irrelevant with
the birth measurements
• Coverage rate based birth intensity update
is proposed to further eliminate the noises
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
–139–
Tracking results comparison for
data sets PETS 2000 and
CAVIAR. (a) Detection results. (b)
Tracking results with birth process
proposed in [13]. (c) Tracking
results with birth process proposed
in this paper.
12:15–12:30 WedCT1.6
A Heteroscedastic Approach to Independent
Motion Detection for Actuated Visual Sensors
Carlo Ciliberto, Sean Ryan Fanello, Lorenzo Natale
and Giorgio Metta
Robotics Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Italy
• A compositional framework is presented to
perform real-time independent motion
detection for applications in Robotics.
• The algorithm can be easily adapted to a
wide range of robotic platforms thanks to
the flexibility granted by its modular
structure.
• The proposed method overcomes the
shortcomings of the current state of the art
approaches by exploiting the known robot
kinematics to predict egomotion rather
than relying on vision alone.
• A heteroscedastic learning layer is
employed to tune the egomotion predictive
capabilities of the system.
Experiments were conducted on
the iCub humanoid robot.