Abstract
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 significantly 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 further 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.
The first author is a student. This work is supported in part by the National Natural Science Foundation of China (No. 61876152 ).
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Gao, C., Wang, P., Gao, Y. (2019). MobileCount: An Efficient Encoder-Decoder Framework for Real-Time Crowd Counting. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_50
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