FCHD: Fast and accurate head detection in crowded scenes

Aditya Vora, Vinay Chilaka
9/24/2018
cs.CV

Official code: aditya-vora/FCHD-Fully-Convolutional-Head-Detector (643 stars, last commit 2022-12-15).

Abstract

In this paper, we propose FCHD-Fully Convolutional Head Detector, an end-to-end trainable head detection model. Our proposed architecture is a single fully convolutional network which is responsible for both bounding box prediction and classification. This makes our model lightweight with low inference time and memory requirements. Along with run-time, our model has better overall average precision (AP) which is achieved by selection of anchor sizes based on the effective receptive field of the network. This can be concluded from our experiments on several head detection datasets with varying head counts. We achieve an AP of 0.70 on a challenging head detection dataset which is comparable to some standard benchmarks. Along with this our model runs at 5 FPS on Nvidia Quadro M1000M for VGA resolution images. Code is available at https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector.

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Code Implementations(1)

aditya-vora/FCHD-Fully-Convolutional-Head-DetectorOfficial100%

Code for FCHD - A fast and accurate head detector

643180Python, Jupyter NotebookSep 23, 20183 years agoNOASSERTION
convolutional-neural-networkscrowd-countingdeep-learningfaster-rcnnhead-detection

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Cite this paper

@article{vora2018fchd,
  title  = {FCHD: Fast and accurate head detection in crowded scenes},
  author = {Aditya Vora and Vinay Chilaka},
  year   = {2018},
  eprint = {1809.08766},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1809.08766}
}

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