Pedestrian Detection and Tracking

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Author(s): Pranali Dhawas (G.H. Raisoni College of Engineering, India), Gopal Kumar Gupta (Symbiosis International University, India), Abhijeet Shrikrishna Kokare (MIT World Peace University, India), Pooja Pimpalshende (Suryodaya College of Engineering and Technology, India), Raju Pawar (G.H. Raisoni College of Engineering, India)and Jatin Jangid (G.H. Raisoni College of Engineering, India)
Copyright: 2025
Pages: 38
Source title: Modern Advancements in Surveillance Systems and Technologies
Source Author(s)/Editor(s): Dina Darwish (Ahram Canadian University, Egypt)
DOI: 10.4018/979-8-3693-6996-8.ch005

Keywords: Information Science Reference / Security & Forensics / Surveillance & Monitoring / Surveillance Systems

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Abstract

Pedestrian detection and tracking are critical components of modern surveillance systems, playing a vital role in various applications such as public safety, autonomous driving, and urban planning. This chapter delves into the fundamental concepts, methodologies, and technological advancements that have shaped the field of pedestrian detection and tracking. Beginning with an overview of traditional methods, including background subtraction and feature-based approaches, the chapter transitions into contemporary deep learning techniques that have significantly improved detection accuracy and robustness. Key algorithms, such as Convolutional Neural Networks (CNNs), Region-based CNNs (R-CNNs), and more recent advancements like Transformer-based models, are explored in detail. The chapter also addresses the integration of these algorithms into real-time tracking systems, discussing object association techniques, motion models, and multi-object tracking strategies.

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