17-09-2014, 01:54 PM
OBJECT TRACKING IN VIDEO SEQUENCES
OBJECT TRACKING.pdf (Size: 1.11 MB / Downloads: 14)
ABSTRACT
This paper gives an overview of Object tracking, the problem of estimating the
trajectory of an object in the image plane as it moves around a scene.. In this paper, we
divide the tracking methods into three categories based on the use of object
representations, namely, methods establishing point correspondence, methods using
primitive geometric models, and methods using contour evolution. Note that all these
classes require object detection at some point. Recognizing the importance of object
detection for tracking systems, we include a short discussion on popular object detection
methods. This paper includes discussion on the object representations, motion models,
and the parameter estimation schemes employed by the tracking algorithms.
INTRODUCTION
Object tracking is an important task within the field of computer vision. The
proliferation of highpowered computers, the availability of high quality and inexpensive
video cameras, and the increasing need for automated video analysis has generated a great
deal of interest in object tracking algorithms. There are three key steps in video analysis:
1. Detection of interesting moving objects,
2. Tracking of such objects from frame to frame, and
3. Analysis of object tracks to recognize their behaviour.
In its simplest form, tracking can be defined as the problem of estimating the
trajectory of an object in the image plane as it moves around a scene. In other words, a
tracker assigns consistent labels to the tracked objects in different frames of a video.
Additionally, depending on the tracking domain, a tracker can also provide objectcentric
information, such as orientation, area, or shape of an object.
One can simplify tracking by imposing constraints on the motion and/or
appearance of objects. For example, almost all tracking algorithms assume that the object
motion is smooth with no abrupt changes. One can further constrain the object motion to
be of constant velocity or constant acceleration based on a priori information. Prior
knowledge about the number and the size of objects, or the object appearance and shape,
can also be used to simplify the problem.
Templates:
Templates are formed using simple geometric shapes or silhouettes. An advantage
of a template is that it carries both spatial and appearance information. Templates,
however, only encode the object appearance generated from a single view. Thus, they are
only suitable for tracking objects whose poses do not vary considerably during the course
of tracking.
FEATURE SELECTION FOR TRACKING
Selecting the right features plays a critical role in tracking. In general, the most
desirable property of a visual feature is its uniqueness so that the objects can be easily
distinguished in the feature space. Feature selection is closely related to the object
representation. For example, color is used as a feature for histogrambased appearance
representations, while for contourbased representation, object edges are usually used as
features. In general, many tracking algorithms use a combination of these features. The
details of common visual features are as follows.
CONCLUSION
Object tracking means tracing the progress of objects (or object features) as they move
about in a visual scene. Object tracking, thus, involves processing spatial (space) as well
as temporal (time) changes. This processing is usually done digitally on a general or a
special purpose computer. Applications of object tracking are numerous and they span a
wide range of domains. In order to track objects, certain features of those objects have to
be selected. However, because of object (and possibly camera) motion, these features have
to be identifiable under varying poses. Furthermore, these features need to be matched
over different frames. Significant progress has been made in object tracking during the
last few years. Several robust trackers have been developed which can track objects in real
time in simple scenarios.