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

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, Yikui Zhai
Copyright: © 2019 |Volume: 10 |Issue: 4 |Pages: 15
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781522565093|DOI: 10.4018/IJACI.2019100103
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MLA

Wang, Tianlei, et al. "A Novel Sparse Representation Based Visual Tracking Method for Dynamic Overhead Cranes: Visual Tracking Method for Dynamic Overhead Cranes." IJACI vol.10, no.4 2019: pp.45-59. http://doi.org/10.4018/IJACI.2019100103

APA

Wang, T., Tan, N., Zhang, C., Li, Y., & Zhai, Y. (2019). A Novel Sparse Representation Based Visual Tracking Method for Dynamic Overhead Cranes: Visual Tracking Method for Dynamic Overhead Cranes. International Journal of Ambient Computing and Intelligence (IJACI), 10(4), 45-59. http://doi.org/10.4018/IJACI.2019100103

Chicago

Wang, Tianlei, et al. "A Novel Sparse Representation Based Visual Tracking Method for Dynamic Overhead Cranes: Visual Tracking Method for Dynamic Overhead Cranes," International Journal of Ambient Computing and Intelligence (IJACI) 10, no.4: 45-59. http://doi.org/10.4018/IJACI.2019100103

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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.

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