A Novel Sparse Representation Based Visual Tracking Method for Dynamic Overhead Cranes: Visual Tracking Method for Dynamic Overhead Cranes

Tianlei Wang, Nanlin Tan, Chi Zhang, Ye Li and Yikui Zhai
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Tianlei Wang: School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China & Department of Intelligent Manufacturing, Wuyi University, Jiangmen, China
Nanlin Tan: School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China
Chi Zhang: School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China
Ye Li: Department of Intelligent Manufacturing, Wuyi University, Jiangmen, China
Yikui Zhai: Department of Intelligent Manufacturing, Wuyi University, Jiangmen, China

International Journal of Ambient Computing and Intelligence (IJACI), 2019, vol. 10, issue 4, 45-59

Abstract: Efficient tracking of heavy loads is the overall goal for overhead cranes in the workplace. This article presents a new way to simply and effectively track overhead cranes at higher speeds. A real-time tracking method is presented here to make tracking the overhead crane load not only accurate, but also faster. First, to observe and extract details and information to use in the appearance model, a measurement matrix is constructed here. Second, a sparse representation is also adopted to track dynamic overhead crane features effectively. Finally, a Naïve Bayesian classifier has been used as a binary classification in a compressed domain. Experiments on the VOT2013 benchmark and constructed data Cranes40, the results demonstrate that the presented sparse representation tracking method can successfully track cranes in real-time.

Date: 2019
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