Visual Object Tracking: An Evaluation Perspective by Xin Zhao (.PDF)

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File Size: 21.1 MB

Visual Object Tracking: An Evaluation Perspective (Advances in Computer Vision and Pattern Recognition) by Xin Zhao, Shiyu Hu, Xu-Cheng Yin
Requirements: .PDF reader, 21.1 MB
Overview: This book delves into visual object tracking (VOT), a fundamental aspect of computer vision crucial for replicating human dynamic vision, with applications ranging from self-driving vehicles to surveillance systems. Despite significant strides propelled by Deep Learning, challenges such as target deformation and motion persist, exposing a disparity between cutting-edge VOT systems and human performance. This observation underscores the necessity to thoroughly scrutinize and enhance evaluation methodologies within VOT research. Hence, the primary objective of this book is to equip readers with essential insights into dynamic visual tasks encapsulated by VOT. Beginning with the elucidation of task definitions, it integrates interdisciplinary perspectives on evaluation techniques. Pursuing dynamic visual intelligence represents one of the most significant challenges in Artificial Intelligence (AI), requiring the synthesis of perceptual acuity, cognitive reasoning, and real-time adaptability. With applications spanning autonomous systems, augmented reality, and surveillance, the ability to emulate human-like visual intelligence has become an essential benchmark for advancing AI. Despite considerable progress in algorithmic innovations and multimodal integration, achieving seamless interaction between human adaptability and machine consistency remains elusive. This book addresses these challenges, positioning itself at the intersection of cognitive science, computer vision, and Machine Learning.
Genre: Non-Fiction > Tech & Devices

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