Real-Time Scale Adaptive Visual Tracking with Context Information and Correlation Filters

2019 ◽  
pp. 93-105
Author(s):  
Yue Xu ◽  
Mengru Feng ◽  
Jiezhi Yang ◽  
Mingjie Peng ◽  
Jiatian Pi ◽  
...  
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Time Scale ◽  
Context Information ◽  
Correlation Filters ◽  
Real Time Scale

Real-time scale-adaptive correlation filters tracker with depth information to handle occlusion

2016 ◽  
Vol 25 (4) ◽  
pp. 043022 ◽  
Author(s):  
Jiatian Pi ◽  
Yuzhang Gu ◽  
Keli Hu ◽  
Xiaoliu Cheng ◽  
Yunlong Zhan ◽  
...  
Keyword(s):  
Real Time ◽  
Time Scale ◽  
Depth Information ◽  
Correlation Filters ◽  
Real Time Scale

Method and device for real-time scale measurements: surface optical properties of small biological objects

1994 ◽  
Author(s):  
Alexander S. Pekarik ◽  
Stanislav G. Rozuvan ◽  
Eugene A. Tikhonov
Keyword(s):  
Optical Properties ◽  
Real Time ◽  
Time Scale ◽  
Biological Objects ◽  
Real Time Scale ◽  
Surface Optical

Real-time visual tracking using complementary kernel support correlation filters

2019 ◽  
Vol 14 (2) ◽  
pp. 417-429 ◽  
Author(s):  
Zhenyang Su ◽  
Jing Li ◽  
Jun Chang ◽  
Bo Du ◽  
Yafu Xiao
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Correlation Filters

scholarly journals In Situ TEM Study on the Growth Process of Amorphous Layer on SnO 2 Nanoparticle During Sodiation on Real Time Scale

2016 ◽  
Vol 22 (S3) ◽  
pp. 1366-1367 ◽  
Author(s):  
Jun Young Cheong ◽  
Joon Ha Chang ◽  
Jeong Yong Lee ◽  
Il-Doo Kim
Keyword(s):  
Real Time ◽  
Time Scale ◽  
Growth Process ◽  
Amorphous Layer ◽  
In Situ Tem ◽  
Real Time Scale

A System for Recording Air Concentrations of Zinc Sulfide Fluorescent Pigment on a Real-Time Scale

1967 ◽  
Vol 6 (2) ◽  
pp. 430-433 ◽  
Author(s):  
P. W. Nickola ◽  
M. O. Rankin ◽  
M. F. Scoggins ◽  
E. M. Sheen
Keyword(s):  
Real Time ◽  
Zinc Sulfide ◽  
Time Scale ◽  
Fluorescent Pigment ◽  
Real Time Scale ◽  
Air Concentrations

scholarly journals MHD simulation of flare situation above the active region AR 10365 in the real time scale.

2020 ◽  
Author(s):  
A.I. Podgorny ◽  
◽  
I.M. Podgorny ◽  
A.V. Borisenko ◽  
◽  
...  
Keyword(s):  
Magnetic Field ◽  
Active Region ◽  
Real Time ◽  
Time Scale ◽  
Rapid Change ◽  
Field Configuration ◽  
Mhd Simulation ◽  
The Real ◽  
Real Time Scale ◽  
The Magnetic Field

Since the configuration of the magnetic field in the corona, where solar flares appear, cannot be determined from observations, to study the flare situation, a numerical magnetohydrodynamic (MHD) simulation is carried out above the active region. MHD simulation performed in a greatly reduced (10 000 times) time scale permit to obtain results on the study of the solar flare mechanism, but the magnetic field configuration was distorted, especially near the photospheric boundary, due to the unnaturally rapid change in the field on the photosphere. For a more accurate study of the flare situation, MHD simulation in the real time scale was performed above the active region of AR 10365, which was made possible through the use of parallel calculations. The MHD simulation in the real scale of time above the AR 10365 during the first day of evolution showed the appearance of current density maxima with singular X-type line and plasma flow, which have to cause to the formation of a current sheet.

scholarly journals Real-Time Visual Tracking with Variational Structure Attention Network

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4904 ◽  
Author(s):  
Yeongbin Kim ◽  
Joongchol Shin ◽  
Hasil Park ◽  
Joonki Paik
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Boundary Effect ◽  
Online Training ◽  
Correlation Filter ◽  
Shape Distortion ◽  
Correlation Filters ◽  
Attention Network ◽  
Real Time Processing ◽  
Variational Structure

Online training framework based on discriminative correlation filters for visual tracking has recently shown significant improvement in both accuracy and speed. However, correlation filter-base discriminative approaches have a common problem of tracking performance degradation when the local structure of a target is distorted by the boundary effect problem. The shape distortion of the target is mainly caused by the circulant structure in the Fourier domain processing, and it makes the correlation filter learn distorted training samples. In this paper, we present a structure–attention network to preserve the target structure from the structure distortion caused by the boundary effect. More specifically, we adopt a variational auto-encoder as a structure–attention network to make various and representative target structures. We also proposed two denoising criteria using a novel reconstruction loss for variational auto-encoding framework to capture more robust structures even under the boundary condition. Through the proposed structure–attention framework, discriminative correlation filters can learn robust structure information of targets during online training with an enhanced discriminating performance and adaptability. Experimental results on major visual tracking benchmark datasets show that the proposed method produces a better or comparable performance compared with the state-of-the-art tracking methods with a real-time processing speed of more than 80 frames per second.

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