NDLI: DCF with high-speed spatial constraint

Content Provider	IET Digital Library
Author	Liu, Jiaqi Bai, Lianfa Zhang, Yi Han, Jing
Abstract	Spatially regularised discriminative correlation filters (SRDCFs) introduce spatial regularisation weights to mitigate the boundary effects caused by circular convolution which obtains superior performance. However, spatial regularisation is computationally expensive; this limits the real-time performance of SRDCF. This study proposes high-speed spatial constraint to DCFs (HSCDCFs) for tracking. Using a large area of the sample to learn a CF, then, the authors introduce the spatial constraint to penalise CF coefficients. Their method formulation allows the CFs to efficiently learn a mass of negative samples and high-quality positive samples. They perform experiments on two benchmark datasets: OTB-2013 and OTB-2015. Compared to SRDCF, they provide a slightly reduce of 2.7 and 3.1%, respectively, in mean overlap precision, their method obtains the real-time speed of 62.5 fps which is ten times faster than SRDCF.
Starting Page	44
Ending Page	48
Page Count	5
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 1, Jan (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5490
Journal	IET Image Processing
Publisher Date	2018-09-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Boundary Effect Mitigation Circular Convolution Convolution Correlation Method Filtering Method in Signal Processing Filtering Theory High-quality Positive Sample High-speed Spatial Constraint HSCDCF OTB-2013 Benchmark Dataset OTB-2015 Benchmark Dataset Real-time Performance Real-time Speed Signal Processing Theory Spatial Regularisation Weight Spatially Regularised Discriminative Correlation Filter
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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