Crowd counting algorithms have recently incorporated attention mechanisms into convolutional neural networks (CNNs) to achieve significant progress. The channel attention model (CAM), as a popular attention mechanism, calculates a set of probability weights to select important channel-wise feature responses. However, most CAMs roughly assign a weight to the entire channel-wise map, which makes useful and useless information being treat indiscriminately, thereby limiting the representational capacity of networks. In this paper, we propose a multi -scale and spatial position-based channel attention network (MS-SPCANet), which integrates spatial position -based channel attention models (SPCAMs) with multiple scales into a CNN. SPCAM assigns different channel attention weights to different positions of channel-wise maps to capture more informative features. Furthermore, an adaptive loss, which uses adaptive coefficients to combine density map loss and headcount loss, is constructed to improve network performance in sparse crowd scenes. Experimental results on four public datasets verify the superiority of the scheme.
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