RSANET: DEEP RECURRENT SCALE-AWARE NETWORK FOR CROWD COUNTING

Most recent works have made significant progress in crowd counting by fusing multi-scale features directly with weighted sum or concatenation to handle large scale variation problems. Meanwhile, there is very little attention paid on the prediction of high-resolution density maps and predicted low-resolution density maps lead to inaccurate counting results. In this paper, we present a novel recurrent scale-aware network(RSANet) to generate a high-resolution density map with scale-aware feature fusion approach. Within this network, we introduce a coarse-to-fine scheme restoring the high-resolution feature map from a low-resolution feature map progressively with stacked dilated convolution blocks. Then, we incorporate recurrent modules to capture dynamic scale-aware information and to benefit the restoration of high-resolution feature maps through multi-scale feature fusion to generate a high-resolution density map. We also use a multi-resolution supervision strategy for training to improve the performance of our network. Extensive experiments on three challenging crowd counting datasets demonstrate the effectiveness of the proposed method.