Can I read The Effect of a Diverse Dataset for Transfer Learning in Thermal Person Detection on EtoBox?

The Effect of a Diverse Dataset for Transfer Learning in Thermal Person Detection by Noor Ul Huda; Bolette D. Hansen; Rikke Gade; Thomas B. Moeslund is a Engineering article available to read on EtoBox.

What is The Effect of a Diverse Dataset for Transfer Learning in Thermal Person Detection about?

Thermal cameras are popular in detection for their precision in surveillance in the dark and for privacy preservation. In the era of data driven problem solving approaches, manually finding and annotating a large amount of data is inefficient in terms of cost and effort. With the introduction of transfer learning, rather than having large datasets, a dataset covering all characteristics and aspects of the target place is more important. In this work, we studied a large thermal dataset recorded for 20 weeks and identified nine phenomena in it. Moreover, we investigated the impact of each phenomenon for model adaptation in transfer learning. Each phenomenon was investigated separately and in combination. the performance was analyzed by computing the F1 score, precision, recall, true negative rate, and false negative rate. Furthermore, to underline our investigation, the trained model with our dataset was further tested on publicly available datasets, and encouraging results were obtained. Finally, our dataset was also made publicly available.

Who reads The Effect of a Diverse Dataset for Transfer Learning in Thermal Person Detection?

Author

Noor Ul Huda; Bolette D. Hansen; Rikke Gade; Thomas B. Moeslund

Publisher

Molecular Diversity Preservation International; MDPI AG; Multidisciplinary Digital Publishing Institute (MDPI); Basel: Molecular Diversity Preservation International (MDPI), 2001-; Publons (ISSN 1424-8220)

Published

2020

Language

EN

Field

Engineering (Physical Sciences)