Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting
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In this paper, we proposed two modified neural network architectures based on SFANet and SegNet respectively for accurate and efficient crowd counting. Inspired by SFANet, the first model is attached with two novel multi-scale-aware modules called, ASSP and CAN. This model is called M-SFANet. The encoder of M-SFANet is enhanced with ASSP containing parallel atrous convolution with different sampling rates and hence able to extract multi-scale features of the target object and incorporate larger context. To further deal with scale variation throughout an input image, we leverage contextual module called CAN which adaptively encodes the scales of the contextual information. The combination yields an effective model for counting in both dense and sparse crowd scenes. Based on SFANet decoder structure, M-SFANet decoder has dual paths, for density map generation and attention map generation. The second model is called M-SegNet. For M-SegNet, we simply change bilinear upsampling used in SFANet to max unpooling originally from SegNet and propose the faster model while providing competitive counting performance. Designed for high-speed surveillance applications, M-SegNet has no additional multi-scale-aware module in order to not increase the complexity. Both models are encoder-decoder based architectures and end-to-end trainable. We also conduct extensive experiments on four crowd counting datasets and one vehicle counting dataset to show that these modifications yield algorithms that could outperform some of state-of-the-art crowd counting methods.
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