What does it take to move from recognizing static objects in images to truly understanding dynamic human behavior in complex scenes? This book provides the answer by introducing a groundbreaking framework that fuses image engineering with spatiotemporal behavior understanding (STBU), offering an in-depth exploration of how action, interaction, and context converge in real-world image analysis. Positioned at the intersection of image understanding, neural networks, and behavioral modeling, this volume equips researchers and engineers with the principles, methods, and architectures needed to analyze and interpret dynamic visual data. It guides readers through each stage of the pipeline--from interest point detection and trajectory learning to action classification, activity modeling, and human-object interaction analysis--culminating in advanced topics such as abnormal event detection and graph-based neural modeling. Throughout, the book introduces deep learning strategies for action and behavior recognition, high-order modeling techniques that integrate motion, posture, and context, and transformer-based approaches for human-object interaction. It also addresses practical challenges such as differential explosion and adapting recognition models to varied scene content. This book is essential reading for graduate students, researchers, and practitioners in computer vision, artificial intelligence, and robotics who seek a comprehensive yet accessible guide to high-level image understanding. A working knowledge of machine learning and basic computer vision concepts is recommended for full benefit. Whether you're advancing academic research or building real-world intelligent systems, this volume provides both the theoretical insight and applied techniques to push the frontier of spatiotemporal image understanding.
