MobileCount: An efficient encoder-decoder framework for real-time crowd counting

Peng Wang

doi:10.1016/j.neucom.2020.05.056

MobileCount: An efficient encoder-decoder framework for real-time crowd counting

2020-09-01

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DOI：10.1016/j.neucom.2020.05.056

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In this work, we propose a computation-efficient encoder-decoder architecture, named MobileCount, which is specifically designed for high-accuracy real-time crowd counting on mobile or embedded devices with limited computation resources. For the encoder part, MobileNetV2 is tailored in order to sig-nificantly reduce FLOPs at a little cost of performance drop, which has 4 bottleneck blocks preceded by a max pooling layer of stride 2. The design of decoder is motivated by Light-weight RefineNet, which fur-ther boosts counting performance with only a 10% increase of FLOPs. In comparison with state-of-the-arts, our proposed network is able to achieve comparable counting performance with 1/10 FLOPs on a number of benchmarks. At last, we propose a multi-layer knowledge distillation method to further boost the performance of MobileCount without increasing its FLOPs. (C) 2020 Elsevier B.V. All rights reserved.

Keywords:

Crowd counting

Light-weight Neural Networks

Fully convolutional networks

Knowledge distillation

MobileCount: An efficient encoder-decoder framework for real-time crowd counting

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