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An End-to-End Transformer Model for Crowd Localization

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13661))

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Abstract

Crowd localization, predicting head positions, is a more practical and high-level task than simply counting. Existing methods employ pseudo-bounding boxes or pre-designed localization maps, relying on complex post-processing to obtain the head positions. In this paper, we propose an elegant, end-to-end Crowd Localization TRansformer named CLTR that solves the task in the regression-based paradigm. The proposed method views the crowd localization as a direct set prediction problem, taking extracted features and trainable embeddings as input of the transformer-decoder. To reduce the ambiguous points and generate more reasonable matching results, we introduce a KMO-based Hungarian matcher, which adopts the nearby context as the auxiliary matching cost. Extensive experiments conducted on five datasets in various data settings show the effectiveness of our method. In particular, the proposed method achieves the best localization performance on the NWPU-Crowd, UCF-QNRF, and ShanghaiTech Part A datasets.

Project page at https://dk-liang.github.io/CLTR/.

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Notes

  1. 1.

    Here, we ignore the \(\hat{C_j}\) for simply illustrating since heads usually report similar confidence score.

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Acknowledgment

This work was supported by National Key R &D Program of China (Grant No. 2018YFB1004602).

Author information

Authors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, 430074, China

    Dingkang Liang & Xiang Bai

  2. Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Wei Xu

Authors
  1. Dingkang Liang
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  2. Wei Xu
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  3. Xiang Bai
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Corresponding author

Correspondence to Xiang Bai .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Liang, D., Xu, W., Bai, X. (2022). An End-to-End Transformer Model for Crowd Localization. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13661. Springer, Cham. https://doi.org/10.1007/978-3-031-19769-7_3

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  • DOI: https://doi.org/10.1007/978-3-031-19769-7_3

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