ADCrowdNet: An Attention-Injective Deformable Convolutional Network for Crowd Understanding

By Ning Liu, Yongchao Long, Changqing Zou, Qun Niu, Li Pan, Hefeng Wu. Computer Vision and Pattern Recognition, 2018

310 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f907826ba0d64dc3bc385088a30e5a492482bc2c

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Citations in the corpus

JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method (2020, background): “Recent approaches like [31, 59, 60, 61, 62, 63] have aimed at incorporating various forms of related information like attention [59], semantic priors [60], segmentation [61], inverse attention [62], and hierarchical attention [31] respectively into the network.”
Attention Scaling for Crowd Counting (2020, methodology): “These methods have tried to exploit multi-scale feature fusion [49, 29, 35], multi-task learning [34, 30, 21], and the attention mechanism [45, 44, 19] to solve the above questions.”
Distribution Matching for Crowd Counting (2020, background): “Almost all prior crowd density map estimation methods [56, 57, 60, 38, 20, 33, 35, 4, 28, 50, 27, 40, 29, 26] have followed this convention.”