Next-Generation Computer Vision Techniques for Video Analysis

Cameras, connected devices, and sensor networks have multiplied fast, and the sheer volume of video data they generate has outgrown what traditional monitoring methods can handle. Simply detecting objects in a frame is not enough anymore. Recent advances in computer vision are pushing systems toward something closer to genuine understanding, recognizing activities, tracking events, and interpreting how people and objects interact within complex, changing environments. That shift is what is enabling a new generation of video analytics, ones that can maintain real situational awareness rather than just flag movement. The applications are already spreading fast, from security and transportation to healthcare, industry, and smart city environments, all relying on systems that can make sense of visual data in real time. Next-Generation Computer Vision Techniques for Video Analysis bridges the gap between theoretical deep learning architectures and their practical application in high-stakes environments, synthesizing fragmented research on temporal modeling to move beyond frame-by-frame analysis toward holistic video understanding. By connecting real-world challenges with a framework for ethical AI deployment, this book equips readers to build privacy-preserving video analytics systems that address bias mitigation in automated surveillance. Covering topics such as activity recognition, video segmentation, and vision language models, this book is an indispensable academic resource for graduate and doctoral students, computer vision researchers, security and surveillance system developers, smart city engineers, industrial automation engineers, policymakers, and more.