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ICPR
2004
IEEE
15 years 10 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»
15 years 4 months 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
14 years 9 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
15 years 11 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»
14 years 11 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...