Spatiotemporal Dilated Convolution with Uncertain Matching for Video-based Crowd Estimation

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4 Scopus citations


In this paper, we propose a novel SpatioTemporal convolutional Dense Network (STDNet) to address the video-based crowd counting problem, which contains the decomposition of 3D convolution and the 3D spatiotemporal dilated dense convolution to alleviate the rapid growth of the model size caused by the Conv3D layer. Moreover, since the dilated convolution extracts the multiscale features, we combine the dilated convolution with the channel attention block to enhance the feature representations. Due to the error that occurs from the difficulty of labeling crowds, especially for videos, imprecise or standard-inconsistent labels may lead to poor convergence for the model. To address this issue, we further propose a new patch-wise regression loss (PRL) to improve the original pixel-wise loss. Experimental results on three video-based benchmarks, i.e., the UCSD, Mall and WorldExpo'10 datasets, show that STDNet outperforms both image- and video-based state-of-the-art methods.

Original languageEnglish
Number of pages14
JournalIEEE Transactions on Multimedia
StateE-pub ahead of print - 8 Jan 2021


  • Annotations
  • Convolution
  • Crowd counting
  • density map regression
  • dilated convolution
  • Feature extraction
  • patch-wise regression loss
  • spatiotemporal modeling
  • Spatiotemporal phenomena
  • Three-dimensional displays
  • Training
  • Videos


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