Abstract: Crowd analysis plays a crucial role in various domains, including security, transportation, and social behavior understanding. Deep learning techniques have emerged as a powerful tool for handling the complexities and challenges associated with crowd analysis tasks. This survey report delves into the recent advancements in deep learning techniques for crowd analysis, highlighting their applications, strengths, and limitations. We explore various approaches used in crowd counting, crowd behavior understanding, and crowd anomaly detection. Additionally, we discuss the datasets commonly employed for evaluating these techniques. By shedding light on the current state-of-the-art, this survey aims to provide insights into the future prospects of deep learning in crowd analysis.
Keywords: Crowd Analysis, Deep Learning, Crowd Detection, Challenges, Convolutional Neural Network.
|
DOI:
10.17148/IJARCCE.2023.12729
