CountNet: End to End Deep Learning for Crowd Counting

Bryan Wilie, Samuel Cahyawijaya, Widyawardana Adiprawita


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.


transfer learning; crowd counting; deep learning

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