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In its early years, the field of computer vision was largely
motivated by researchers seeking computational models of biological
vision and solutions to practical problems in manufacturing,
defense, and medicine. For the past two decades or so, there has
been an increasing interest in computer vision as an input modality
in the context of human-computer interaction. Such vision-based
interaction can endow interactive systems with visual capabilities
similar to those important to human-human interaction, in order to
perceive non-verbal cues and incorporate this information in
applications such as interactive gaming, visualization, art
installations, intelligent agent interaction, and various kinds of
command and control tasks. Enabling this kind of rich, visual and
multimodal interaction requires interactive-time solutions to
problems such as detecting and recognizing faces and facial
expressions, determining a person's direction of gaze and focus of
attention, tracking movement of the body, and recognizing various
kinds of gestures. In building technologies for vision-based
interaction, there are choices to be made as to the range of
possible sensors employed (e.g., single camera, stereo rig, depth
camera), the precision and granularity of the desired outputs, the
mobility of the solution, usability issues, etc. Practical
considerations dictate that there is not a one-size-fits-all
solution to the variety of interaction scenarios; however, there
are principles and methodological approaches common to a wide range
of problems in the domain. While new sensors such as the Microsoft
Kinect are having a major influence on the research and practice of
vision-based interaction in various settings, they are just a
starting point for continued progress in the area. In this book, we
discuss the landscape of history, opportunities, and challenges in
this area of vision-based interaction; we review the
state-of-the-art and seminal works in detecting and recognizing the
human body and its components; we explore both static and dynamic
approaches to "looking at people" vision problems; and we place the
computer vision work in the context of other modalities and
multimodal applications. Readers should gain a thorough
understanding of current and future possibilities of computer
vision technologies in the context of human-computer interaction.
As networks of video cameras are installed in many applications
like security and surveillance, environmental monitoring, disaster
response, and assisted living facilities, among others, image
understanding in camera networks is becoming an important area of
research and technology development. There are many challenges that
need to be addressed in the process. Some of them are listed below:
- Traditional computer vision challenges in tracking and
recognition, robustness to pose, illumination, occlusion, clutter,
recognition of objects, and activities; - Aggregating local
information for wide area scene understanding, like obtaining
stable, long-term tracks of objects; - Positioning of the cameras
and dynamic control of pan-tilt-zoom (PTZ) cameras for optimal
sensing; - Distributed processing and scene analysis algorithms; -
Resource constraints imposed by different applications like
security and surveillance, environmental monitoring, disaster
response, assisted living facilities, etc. In this book, we focus
on the basic research problems in camera networks, review the
current state-of-the-art and present a detailed description of some
of the recently developed methodologies. The major underlying theme
in all the work presented is to take a network-centric view whereby
the overall decisions are made at the network level. This is
sometimes achieved by accumulating all the data at a central
server, while at other times by exchanging decisions made by
individual cameras based on their locally sensed data. Chapter One
starts with an overview of the problems in camera networks and the
major research directions. Some of the currently available
experimental testbeds are also discussed here. One of the
fundamental tasks in the analysis of dynamic scenes is to track
objects. Since camera networks cover a large area, the systems need
to be able to track over such wide areas where there could be both
overlapping and non-overlapping fields of view of the cameras, as
addressed in Chapter Two: Distributed processing is another
challenge in camera networks and recent methods have shown how to
do tracking, pose estimation and calibration in a distributed
environment. Consensus algorithms that enable these tasks are
described in Chapter Three. Chapter Four summarizes a few
approaches on object and activity recognition in both distributed
and centralized camera network environments. All these methods have
focused primarily on the analysis side given that images are being
obtained by the cameras. Efficient utilization of such networks
often calls for active sensing, whereby the acquisition and
analysis phases are closely linked. We discuss this issue in detail
in Chapter Five and show how collaborative and opportunistic
sensing in a camera network can be achieved. Finally, Chapter Six
concludes the book by highlighting the major directions for future
research. Table of Contents: An Introduction to Camera Networks /
Wide-Area Tracking / Distributed Processing in Camera Networks /
Object and Activity Recognition / Active Sensing / Future Research
Directions
Advanced Methods and Deep Learning in Computer Vision presents
advanced computer vision methods, emphasizing machine and deep
learning techniques that have emerged during the past 5-10 years.
The book provides clear explanations of principles and algorithms
supported with applications. Topics covered include machine
learning, deep learning networks, generative adversarial networks,
deep reinforcement learning, self-supervised learning, extraction
of robust features, object detection, semantic segmentation,
linguistic descriptions of images, visual search, visual tracking,
3D shape retrieval, image inpainting, novelty and anomaly
detection. This book provides easy learning for researchers and
practitioners of advanced computer vision methods, but it is also
suitable as a textbook for a second course on computer vision and
deep learning for advanced undergraduates and graduate students.
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