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CVPR
2009
IEEE
3538views Computer Vision» more  CVPR 2009»
17 years 1 months ago
Markerless Motion Capture with Unsynchronized Moving Cameras
In this work we present an approach for markerless motion capture (MoCap) of articulated objects, which are recorded with multiple unsynchronized moving cameras. Instead of usin...
Bodo Rosenhahn, Hans-Peter Seidel, Juergen Gall, M...
ICCV
2009
IEEE
1190views Computer Vision» more  ICCV 2009»
16 years 10 months ago
You'll never walk alone: modeling social behavior for multi-target tracking
Object tracking typically relies on a dynamic model to predict the object’s location from its past trajectory. In crowded scenarios a strong dynamic model is particularly impo...
S. Pellegrini, A. Ess, K. Schindler, L. van Gool
ICCV
2009
IEEE
16 years 10 months ago
Which Faces to Tag: Adding Prior Constraints into Active Learning
We introduce an algorithm that guides the user to tag faces in the best possible order during a face recognition assisted tagging scenario. In particular, we extend the active l...
Ashish Kapoor, Gang Hua, Amir Akbarzadeh and Simon...
CVPR
1998
IEEE
16 years 7 months ago
Joint Probabilistic Techniques for Tracking Multi-Part Objects
Common objects such as people and cars comprise many visual parts and attributes, yet image-based tracking algorithms are often keyed to only one of a target's identifying ch...
Christopher Rasmussen, Gregory D. Hager
CVPR
2001
IEEE
16 years 7 months ago
Learning Models for Object Recognition
We consider learning models for object recognition from examples. Our method is motivated by systems that use the Hausdorff distance as a shape comparison measure. Typically an ob...
Pedro F. Felzenszwalb