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

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arxiv 1811.11968 v5 pith:IFZ3GCGJ submitted 2018-11-29 cs.CV

Ning Liu , Yongchao Long , Changqing Zou , Qun Niu , Li Pan , Hefeng Wu This is my paper · ORCID
classification cs.CV
keywords crowdadcrowdnetdeformablenetworkcalledconvolutionalregionsattention-aware
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Crowd Counting with Deep Structured Scale Integration Network

    cs.CV 2019-08 conditional novelty 6.0 of 10

    DSSINet combines CRF-based multi-scale feature refinement and a dilated MS-SSIM loss to improve crowd counting accuracy on four benchmarks.

  2. Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting

    cs.CV 2019-08 conditional novelty 5.0 of 10

    The proposed MBTTBF-SCFB network achieves lower average counting error than several prior methods on ShanghaiTech, UCF_CC_50, and UCF-QNRF, though it trails CAN on two benchmarks.

  3. Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs

    cs.CV 2019-08 conditional novelty 4.0 of 10

    A three-part CNN with a count attention mechanism routes dense and sparse image regions to networks of different capacities and reports state-of-the-art counting errors on five benchmarks.

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