mlsanityData to 14 Oct 2026

Visual Crowd Analysis: Open Research Problems

arXiv 2308.10677 ↗First posted 21 Aug 2023cs.CV cs.AIcited by 2

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Abstract (arXiv)

Over the last decade, there has been a remarkable surge in interest in automated crowd monitoring within the computer vision community. Modern deep-learning approaches have made it possible to develop fully-automated vision-based crowd-monitoring applications. However, despite the magnitude of the issue at hand, the significant technological advancements, and the consistent interest of the research community, there are still numerous challenges that need to be overcome. In this article, we delve into six major areas of visual crowd analysis, emphasizing the key developments in each of these areas. We outline the crucial unresolved issues that must be tackled in future works, in order to ensure that the field of automated crowd monitoring continues to progress and thrive. Several surveys related to this topic have been conducted in the past. Nonetheless, this article thoroughly examines and presents a more intuitive categorization of works, while also depicting the latest breakthroughs within the field, incorporating more recent studies carried out within the last few years in a concise manner. By carefully choosing prominent works with significant contributions in terms of novelty or performance gains, this paper presents a more comprehensive exposition of advancements in the current state-of-the-art.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 33 of its 85 references that are in the map. First posted on arXiv 21 Aug 2023.

Builds on

33 of 85 references.

References · 33

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21 more
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Cited by year

Citing papers per year: 2024: 2.

2 papers.

  1. 2024Multimodal Crowd Counting with Pix2Pix GANscited by 2
  2. 2024Haris: an Advanced Autonomous Mobile Robot for Smart Parking Assistancecited by 0