  We propose an attention-injective deformable convolutional network called
ADCrowdNet for crowd understanding that can address the accuracy degradation
problem of highly congested noisy scenes. ADCrowdNet contains two concatenated
networks. An attention-aware network called Attention Map Generator (AMG) first
detects crowd regions in images and computes the congestion degree of these
regions. Based on detected crowd regions and congestion priors, a multi-scale
deformable network called Density Map Estimator (DME) then generates
high-quality density maps. With the attention-aware training scheme and
multi-scale deformable convolutional scheme, the proposed ADCrowdNet achieves
the capability of being more effective to capture the crowd features and more
resistant to various noises. We have evaluated our method on four popular crowd
counting datasets (ShanghaiTech, UCF_CC_50, WorldEXPO'10, and UCSD) and an
extra vehicle counting dataset TRANCOS, and our approach beats existing
state-of-the-art approaches on all of these datasets.
