  State-of-the-art crowd counting models follow an encoder-decoder approach.
Images are first processed by the encoder to extract features. Then, to account
for perspective distortion, the highest-level feature map is fed to extra
components to extract multiscale features, which are the input to the decoder
to generate crowd densities. However, in these methods, features extracted at
earlier stages during encoding are underutilised, and the multiscale modules
can only capture a limited range of receptive fields, albeit with considerable
computational cost. This paper proposes a novel crowd counting architecture
(FusionCount), which exploits the adaptive fusion of a large majority of
encoded features instead of relying on additional extraction components to
obtain multiscale features. Thus, it can cover a more extensive scope of
receptive field sizes and lower the computational cost. We also introduce a new
channel reduction block, which can extract saliency information during decoding
and further enhance the model's performance. Experiments on two benchmark
databases demonstrate that our model achieves state-of-the-art results with
reduced computational complexity.
