scholarly journals Feature selection for real-time tracking

2006 ◽  
Author(s):  
D. Frank Hsu ◽  
Damian M. Lyons ◽  
Jizhou Ai
Keyword(s):  
Feature Selection ◽  
Real Time ◽  
Real Time Tracking ◽  
Selection For

Feature Selection for Real-Time Drilling Sensing Systems Based on Linear Discriminants

2020 ◽  
Author(s):  
Ricardo Ramirez ◽  
Ian Michael Soukup ◽  
Rafael Tapia ◽  
Carlos A. Cardona ◽  
Michael Sandford Boudreaux ◽  
...  
Keyword(s):  
Feature Selection ◽  
Real Time ◽  
Sensing Systems ◽  
Selection For ◽  
Linear Discriminants

A Real-Time Visual Tracking Approach Using Sparse Autoencoder and Extreme Learning Machine

2015 ◽  
Vol 03 (04) ◽  
pp. 267-275
Author(s):  
Liang Dai ◽  
Yuesheng Zhu ◽  
Guibo Luo ◽  
Chao He ◽  
Hanchi Lin
Keyword(s):  
Real Time ◽  
Extreme Learning Machine ◽  
Visual Tracking ◽  
Computational Cost ◽  
Real Time Tracking ◽  
Sparse Autoencoder ◽  
Selection For ◽  
Learning Machine ◽  
Hidden Layer ◽  
Online Process

Visual tracking algorithm based on deep learning is one of the state-of-the-art tracking approaches. However, its computational cost is high. To reduce the computational burden, in this paper, A real-time tracking approach is proposed by using three modules: a single hidden layer neural network based on sparse autoencoder, a feature selection for simplifying the network and an online process based on extreme learning machine. Our experimental results have demonstrated that the proposed algorithm has good performance of robust and real-time.

PICASO: PIxel correspondences and SOft match selection for real-time tracking

2016 ◽  
Vol 153 ◽  
pp. 151-162 ◽  
Author(s):  
Radu Timofte ◽  
Junseok Kwon ◽  
Luc Van Gool
Keyword(s):  
Real Time ◽  
Real Time Tracking ◽  
Selection For

Feature Selection for Real-Time NLOS Identification and Mitigation for Body-Mounted UWB Transceivers

2021 ◽  
pp. 1-1
Author(s):  
Andre G. Ferreira ◽  
Duarte Fernandes ◽  
Sergio Branco ◽  
Andre P. Catarino ◽  
Joao L. Monteiro
Keyword(s):  
Feature Selection ◽  
Real Time ◽  
Selection For
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