NDLI: Object tracking based on local feature points

Content Provider	IEEE Xplore Digital Library
Author	Haili Wang Liang Zhang
Copyright Year	2010
Description	Author affiliation: Training Center of Engineering Technology, Civil Aviation University of China, Tianjin, China (Haili Wang) \|\| Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin, China (Liang Zhang)
Abstract	This paper presents a novel local-feature-based algorithm to track objects through frames. Real-time performance and occlusion are great challenges in object tracking. Local features are more distinctive than global features in dealing with occlusion. SURF (Speeded-Up Robust Feature) can robustly identify objects in clutter scene and occlusion. However, initial SURF algorithm has difficulty in matching accurately. Combined NN/SN (ratio of closest and next closes distances) with RANSAC (Random Sample Consensus) algorithm and location correlation of corresponding features between two frames is proposed to reduce false match and speed up the matching procedure. This method exhibits very good performance in high reliable applications, for its effectiveness and reduced complexity. Simulation on PETS database proves it effective.
Starting Page	349
Ending Page	352
File Size	839813
Page Count	4
File Format	PDF
ISBN	9781424465132
e-ISBN	9781424465163
DOI	10.1109/CISP.2010.5648034
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	speeded-up robust feature Computer vision Correlation local featur Signal processing algorithms Artificial neural networks random sample consensus Tin Feature extraction Robustness feature matching video processing
Content Type	Text
Resource Type	Article

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