MULTI-SCALE ATTENTION NETWORK FOR HIGHLY CONGESTED CROWD COUNTING

Aimed at the problem of the poor performance in crowd counting tasks caused by scale various in highly congested scenes, a dense crowd counting model based on multi-scale attention network (MANet) is proposed. A multi-column convolutional neural network was constructed to capture multi scale features and to promote scale information fusion. A dual attention module was adopted to obtain contextual information and enhance the performance of multi scale feature. Dense connection was used to reuse multi scale feature maps, and generate high-quality density maps, and the density maps were integrated to count. A new loss function was proposed, which directly used the dot annotation map for training to reduce the additional error caused by the Gaussian filtering to smooth the dot annotation. The best results on the public datasets (ShanghaiTech Part A/B, UCF-CC-50, UCF-QNRF) demonstrate that our model can effectively handle multi-scale various in highly congested scenes and generate high-quality density maps.