Multi-level features extraction network with gating mechanism for crowd counting

Crowd counting is still a practical and challenging problem owing to scale variations and information loss. Most existing methods based on the straightforward fusion of different features from a deep neural network seem to eliminate this limitation. However, these features are difficult to be fused since they often differ significantly in modality and dimensionality. Unlike previous works, a multi-level features extraction network with gating mechanism for crowd counting is proposed. Specifically, a multi-channel gated unit to adaptively extract features in different levels of the network is proposed, which can avoid interference from confusing information. To fully aggregate features via multi-level fusion, multi-level features extraction scheme is presented. The multi-level features extraction network learns to fuse features from multiple levels and reduce false predictions. Extensive experiments and evaluations clearly illustrate that the proposed approach achieves state-of-the-art counting performance against other methods on four mainstream crowd counting benchmarks.