NDLI: An improved object tracking based on spatial context

Content Provider	IEEE Xplore Digital Library
Author	Bo Xu Zhenhai Wang
Copyright Year	2015
Description	Author affiliation: Coll. of Mech. Eng., Linyi Univ., Linyi, China (Bo Xu; Zhenhai Wang)
Abstract	This paper proposes an improved object tracking method based on spatial context of image to improve the accuracy and real-time of object tracking. First, the image is randomly sampled around target at current frame. We compare each sample with template image using the kernel method in Fourier domain so that we will obtain the location of the maximum response. Then, in this position, the pixel similarity is summed by the weighted Gauss function within 10*10 sub-window, and the location of the maximum similarity in all sampling regards as the best tracking position. The experimental results demonstrate that the tracking speed is obviously improved because Fast Fourier Transform(FFT) is adopted in algorithm. Tracking algorithm runs at about 100 frames per second on i5 machine. Tracker precision reaches about 90% at a threshold of 50. Extensive experimental results show that the proposed algorithm outperforms favorably against state-of-art tracking methods based on kernel method in many complex conditions.
Starting Page	1035
Ending Page	1039
File Size	1167486
Page Count	5
File Format	PDF
e-ISBN	9781467376822
DOI	10.1109/FSKD.2015.7382085
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Target tracking Machine vision Video sequences Fast Fourier Transform (FFT) Real-time systems Classification algorithms Object tracking Kernel Spatial context
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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