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Deep People Counting in Extremely Dense Crowds

Published: 13 October 2015 Publication History

Abstract

People counting in extremely dense crowds is an important step for video surveillance and anomaly warning. The problem becomes especially more challenging due to the lack of training samples, severe occlusions, cluttered scenes and variation of perspective. Existing methods either resort to auxiliary human and face detectors or surrogate by estimating the density of crowds. Most of them rely on hand-crafted features, such as SIFT, HOG etc, and thus are prone to fail when density grows or the training sample is scarce. In this paper we propose an end-to-end deep convolutional neural networks (CNN) regression model for counting people of images in extremely dense crowds. Our method has following characteristics. Firstly, it is a deep model built on CNN to automatically learn effective features for counting. Besides, to weaken influence of background like buildings and trees, we purposely enrich the training data with expanded negative samples whose ground truth counting is set as zero. With these negative samples, the robustness can be enhanced. Extensive experimental results show that our method achieves superior performance than the state-of-the-arts in term of the mean and variance of absolute difference.

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Cited By

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  • (2025)Virtual Classification: Modulating Domain-Specific Knowledge for Multidomain Crowd CountingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.335036336:2(2958-2972)Online publication date: Feb-2025
  • (2025)Deep Rank-Consistent Pyramid Model for Enhanced Crowd CountingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.333677436:1(299-312)Online publication date: Jan-2025
  • (2025)MIANet: Bridging the Gap in Crowd Density Estimation With Thermal and RGB InteractionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.347829226:1(254-267)Online publication date: Jan-2025
  • Show More Cited By

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cover image ACM Conferences
MM '15: Proceedings of the 23rd ACM international conference on Multimedia
October 2015
1402 pages
ISBN:9781450334594
DOI:10.1145/2733373
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 October 2015

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Author Tags

  1. convolutional neural networks(CNN)
  2. crowd analysis
  3. people counting

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  • Short-paper

Funding Sources

  • 100 Talents Programme of The Chinese Academy of Sciences
  • National Natural Science Foundation of China
  • National Training Programs of Innovation and Entrepreneurship for Undergraduates
  • the Young Scholars by the Tianjin University of Commerce

Conference

MM '15
Sponsor:
MM '15: ACM Multimedia Conference
October 26 - 30, 2015
Brisbane, Australia

Acceptance Rates

MM '15 Paper Acceptance Rate 56 of 252 submissions, 22%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

View all
  • (2025)Virtual Classification: Modulating Domain-Specific Knowledge for Multidomain Crowd CountingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.335036336:2(2958-2972)Online publication date: Feb-2025
  • (2025)Deep Rank-Consistent Pyramid Model for Enhanced Crowd CountingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.333677436:1(299-312)Online publication date: Jan-2025
  • (2025)MIANet: Bridging the Gap in Crowd Density Estimation With Thermal and RGB InteractionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.347829226:1(254-267)Online publication date: Jan-2025
  • (2025)A comprehensive survey of crowd density estimation and countingIET Image Processing10.1049/ipr2.1332819:1Online publication date: 27-Jan-2025
  • (2025)Next-generation coupled structure-human sensing technology: Enhanced pedestrian-bridge interaction analysis using data fusion and machine learningInformation Fusion10.1016/j.inffus.2025.102983118(102983)Online publication date: Jun-2025
  • (2024)A Human Face Detector for Big Data Analysis of Pilgrim Flow Rates in Hajj and UmrahEngineering, Technology & Applied Science Research10.48084/etasr.666814:1(12861-12868)Online publication date: 8-Feb-2024
  • (2024)Domain-Agnostic Crowd Counting via Uncertainty-Guided Style Diversity AugmentationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681310(1642-1651)Online publication date: 28-Oct-2024
  • (2024)Confusion Region Mining for Crowd CountingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.331102035:12(18039-18051)Online publication date: Dec-2024
  • (2024)Crowd Counting Based on Multiscale Spatial Guided Perception Aggregation NetworkIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.330434835:12(17465-17478)Online publication date: Dec-2024
  • (2024)A Perspective-Embedded Scale-Selection Network for Crowd Counting in Public TransportationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.332800025:5(3420-3432)Online publication date: May-2024
  • Show More Cited By

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