Karya
Judul/Title Improvement of Deep Learning-based Human Detection using Dynamic Thresholding for Intelligent Surveillance System
Penulis/Author Wahyono, Ph.D. (1) ; Moh. Edi Wibowo, S.Kom.,M.Kom., Ph.D. (2); Prof. Dr. Techn. Ahmad Ashari, M.I.Kom. (3); Muhammad Pajar Kharisma Putra (4)
Tanggal/Date 31 2021
Kata Kunci/Keyword
Abstrak/Abstract Human detection plays an important role in many applications of the intelligent surveillance system (ISS), such as person re-identification, human tracking, people counting, etc. On the other hand, the use of deep learning in human detection has provided excellent accuracy. Unfortunately, the deep learning method is sometimes unable to detect objects that are too far from the camera. It is because the threshold selection for confidence value is statically determined at the decision stage. This paper proposes a new strategy for using dynamic thresholding based on geometry in the images. The proposed method is evaluated using the dataset we created. The experiment found that the use of dynamic thresholding provides an increase in F-measure of 0.11 while reducing false positives by 0.18. This shows that the proposed strategy effectively detects human objects, which is applied to the ISS.
Rumpun Ilmu Ilmu Komputer
Bahasa Asli/Original Language English
Level Internasional
Status
Dokumen Karya
No Judul Tipe Dokumen Aksi
1PEER REVIEW_wahyono 5.pdf[PAK] Peer Review
2Improvement of Deep Learning-based Human Detection using Dynamic Thresholding.pdf[PAK] Full Dokumen