In the human quest for scientific knowledge empirical evidence is collected by visual
perception. Tracking with computer vision takes on the important role to reveal complex
patterns of motion that exist in the world we live in. Multi-object tracking algorithms provide
new information on how groups and individual group members move through three-dimensional
space. They enable us to study in depth the relationships between individuals in moving groups.
These may be interactions of pedestrians on a crowded sidewalk living cells under a microscope
or bats emerging in large numbers from a cave. Being able to track pedestrians is important for
urban planning analysis of cell interactions supports research on biomaterial design and the
study of bat and bird flight can guide the engineering of aircraft. We were inspired by this
multitude of applications to consider the crucial component needed to advance a single-object
tracking system to a multi-object tracking system-data association. Data association in the
most general sense is the process of matching information about newly observed objects with
information that was previously observed about them. This information may be about their
identities positions or trajectories. Algorithms for data association search for matches that
optimize certain match criteria and are subject to physical conditions. They can therefore be
formulated as solving a constrained optimization problem-the problem of optimizing an objective
function of some variables in the presence of constraints on these variables. As such data
association methods have a strong mathematical grounding and are valuable general tools for
computer vision researchers. This book serves as a tutorial on data association methods
intended for both students and experts in computer vision. We describe the basic research
problems review the current state of the art and present some recently developed approaches.
The book covers multi-object tracking in two and three dimensions. We consider two imaging
scenarios involving either single cameras or multiple cameras with overlapping fields of view
and requiring across-time and across-view data association methods. In addition to methods that
match new measurements to already established tracks we describe methods that match trajectory
segments also called tracklets. The book presents a principled application of data association
to solve two interesting tasks: first analyzing the movements of groups of free-flying animals
and second reconstructing the movements of groups of pedestrians. We conclude by discussing
exciting directions for future research.
