CountNet: End to end deep learning for crowd counting
Wilie B.a, Cahyawijaya S.a, Adiprawita W.a
a Bandung Institute of Technology, Bandung, Indonesia
Abstract
© 2018 IEEE.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.
Author keywords
Indexed keywords
Crowd counting,Deep learning,Transfer learning
Funding details
ACKNOWLEDGMENT The Titan XP used for this research was donated by the NVIDIA Corporation, and this work was also supported by Amazon Web Service (AWS) Educate, & Lembaga Pengembangan Inovasi dan Kewirausahaan Institut Teknologi Bandung (LPIK ITB).