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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics
Samuel Cahyawijaya
Institut Teknologi Bandung & Prosa
Computer Science & IT Electrical & Electronics Engineering

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CountNet: End to End Deep Learning for Crowd Counting Bryan Wilie; Samuel Cahyawijaya; Widyawardana Adiprawita
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (722.729 KB) | DOI: 10.11591/eecsi.v5.1704

Abstract

We approach crowd counting problem as a complex end to end deep learning process that needs both a correct recognition and counting. This paper redefines the crowd counting process to be a counting process, rather than just a recognition process as previously defined. Xception Network is used in the CountNet and layered again with fully connected layers. The Xception Network pre-trained parameter is used as transfer learning to be trained again with the fully connected layers. CountNet then achieved a better crowd counting performance by training it with augmented dataset that robust to scale and slice variations.
Co-Authors Bryan Wilie Widyawardana Adiprawita