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Inception-Based Crowd Counting -- Being Fast while Remaining Accurate

arXiv 2210.09796 ↗First posted 18 Oct 2022cs.CVcited by 0

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Abstract (arXiv)

Recent sophisticated CNN-based algorithms have demonstrated their extraordinary ability to automate counting crowds from images, thanks to their structures which are designed to address the issue of various head scales. However, these complicated architectures also increase computational complexity enormously, making real-time estimation implausible. Thus, in this paper, a new method, based on Inception-V3, is proposed to reduce the amount of computation. This proposed approach (ICC), exploits the first five inception blocks and the contextual module designed in CAN to extract features at different receptive fields, thereby being context-aware. The employment of these two different strategies can also increase the model's robustness. Experiments show that ICC can at best reduce 85.3 percent calculations with 24.4 percent performance loss. This high efficiency contributes significantly to the deployment of crowd counting models in surveillance systems to guard the public safety. The code will be available at https://github.com/YIMINGMA/CrowdCounting-ICC,and its pre-trained weights on the Crowd Counting dataset, which comprises a large variety of scenes from surveillance perspectives, will also open-sourced.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 49 references that are in the map. First posted on arXiv 18 Oct 2022.

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References · 24

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  9. 2020Distribution Matching for Crowd Countingcited by 143
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  11. 2018Class-Agnostic Countingcited by 85
  12. 2019Perspective-Guided Convolution Networks for Crowd Countingcited by 54
12 more
  1. 2017DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimationcited by 49
  2. 2018Revisiting Perspective Information for Efficient Crowd Countingcited by 49
  3. 2018Iterative Crowd Countingcited by 48
  4. 2019From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquercited by 48
  5. 2018ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understandingcited by 45
  6. 2017CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Countingcited by 41
  7. 2017Spatiotemporal Modeling for Crowd Counting in Videoscited by 27
  8. 2017TasselNet: Counting maize tassels in the wild via local counts regression networkcited by 26
  9. 2020Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Countingcited by 22
  10. 2018Crowd Counting using Deep Recurrent Spatial-Aware Networkcited by 21
  11. 2019Crowd Counting Using Scale-Aware Attention Networkscited by 20
  12. 2018Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNNcited by 19

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