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ICPR
2004
IEEE
14 years 7 months ago
Probabilistic Object Tracking Using Multiple Features
We present a generic tracker which can handle a variety of different objects. For this purpose, groups of low-level features like interest points, edges, homogeneous and textured ...
David Serby, Esther Koller-Meier, Luc J. Van Gool
IROS
2009
IEEE
266views Robotics» more  IROS 2009»
14 years 26 days ago
Inertial-aided KLT feature tracking for a moving camera
— We propose a novel inertial-aided KLT feature tracking method robust to camera ego-motions. The conventional KLT uses images only and its working condition is inherently limite...
Myung Hwangbo, Jun-Sik Kim, Takeo Kanade
ICCCN
2007
IEEE
13 years 6 months ago
Online Selection of Tracking Features using AdaBoost
In this paper, a novel feature selection algorithm for object tracking is proposed. This algorithm performs more robust than the previous works by taking the correlation between f...
Ying-Jia Yeh, Chiou-Ting Hsu
CVPR
2007
IEEE
14 years 8 months ago
Learning Features for Tracking
We treat tracking as a matching problem of detected keypoints between successive frames. The novelty of this paper is to learn classifier-based keypoint descriptions allowing to i...
Michael Grabner, Helmut Grabner, Horst Bischof
WSCG
2004
232views more  WSCG 2004»
13 years 7 months ago
Robust Tracking of Athletes Using Multiple Features of Multiple Views
This paper presents a robust and reconfigurable object tracker that integrates multiple visual features from multiple views. The tandem modular architecture stepwise refines the e...
Toshihiko Misu, Seiichi Gohshi, Yoshinori Izumi, Y...