ADCrowdNet: An Attention-Injective Deformable Convolutional Network forCrowd Understanding
Abstract
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 multiscale 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 stateof-the-art approaches on all of these datasets.