Visual object tracking, i.e., consistently inferring the motion of a
target from image sequences, is a must-have component to bridge
low-level image processing and high-level video analysis, which gains
great popularity due to its applications in diverse areas, such as
human-computer interaction, security video surveillance, medical image
processing, and robotics. In this book, we focus on how to enhance the
generality and reliability of object-level tracking given no prior
knowledge about targets. We propose two novel ideas: context-aware and
attentional tracking, where the tracker discovers some auxiliary objects
that have short-term motion correlations with the target as the spatial
contexts, or augments the observation models by selectively attending
discriminative regions inside the target, or adaptively tuning the
feature granularity and model elasticity. These approaches achieve
promising results on challenging real-world videos. The book sheds some
light on recent progress of visual tracking and should be useful to
professionals in computer vision research, or anyone else who may be
considering utilizing visual tracking in intelligent video analysis
systems.