CountNet: End to End Deep Learning for Crowd Counting

Bryan Wilie (Bandung Institute of Technology)
Samuel Cahyawijaya (Institut Teknologi Bandung & Prosa)
Widyawardana Adiprawita (Institut Teknologi Bandung)

Proceeding of the Electrical Engineering Computer Science and Informatics
Vol 5: EECSI 2018

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.