Visual Crowd Analysis: Open Research Problems

TL;DR

This paper reviews recent advancements and open challenges in automated visual crowd analysis, emphasizing key developments, unresolved issues, and future research directions in the field of computer vision-based crowd monitoring.

Contribution

It provides a comprehensive, updated categorization of recent works, highlighting significant breakthroughs and outlining unresolved challenges in visual crowd analysis.

Findings

Identifies six major areas of visual crowd analysis.

Highlights recent technological advancements and breakthroughs.

Outlines key unresolved issues for future research.

Abstract

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.