NDLI: Robust object tracking using Bi-model

Content Provider	IEEE Xplore Digital Library
Author	Zhi Zhou Yue Wang Eam Khwang Teoh
Copyright Year	2013
Description	Author affiliation: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore (Zhi Zhou; Eam Khwang Teoh) \|\| Visual Comput. Dept., Inst. for Infocomm Res. (I2R), Singapore, Singapore (Yue Wang)
Abstract	Occlusion is one of the major problems that object tracking faces in a clustered environment. In this paper, a tracking method which can deal with partial occlusion is proposed. There are two novelties in this paper: (1) using SURF keypoints to represent the object, key-points are evaluated and online learned by Random Ferns. (2) Bi-model is proposed to store key-points from object and surrounding background. In each frame, key-points inside or around the object bounding box will be assigned labels by matching with points stored in the Bi-model. These labeled points will be further used for improving the tracking accuracy and learning of Random Ferns. Long-term tracking is achieved by combining detection and tracking together. Experiments on videos with occlusion conditions show that the proposed method has good performance on tracking partial occluded objects, compared to some of the state-of-art methods.
Starting Page	3103
Ending Page	3107
File Size	311159
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738639
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	object detection Object tracking partial occlusion SURF Random Ferns
Content Type	Text
Resource Type	Article

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