[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

2 Design of Object Detection, Recognition and Tracking Algorithms
Visual surveillance is one of the most active fields of research. Its main idea is to
detect any specified targets or abnormal situations in the line of sight. Targets can be
detected due to their characteristic features e.g. face, character of movement, size,
color or shape. Those features are detected by a dedicated set of algorithms
processing images acquired from live video stream. Traditionally detection is performed
within 2D images acquired from the video stream. Recently also depth images
from various structural light or lidar systems are taken into account. Real time
processing is essential for accurate system or human observer reaction therefore the
time of computation is an important limitation that has to be coped with. There are
multiple abnormal situations that might be detected. A typical example is detection of
abandoned objects like suitcases or small size packages that might contain highly
energetic materials. Another example is road surveillance where incidents breaking
the traffic law are aim of the detection process. Similar approach is utilized by city
surveillance systems where average people trajectories are computed and any unusual
activities are detected and reported to the human staff.
Detection is usually a time consuming process thus it is followed by less time
consuming object tracking phase. During tracking previously detected characteristic
object features are tracked from frame to frame usually only in a specified local
surroundings. Forasmuch as tracked object may change its perceived appearance
during tracking it is frequently assumed that the model update is necessary for
maintaining satisfactory quality of the tracking process. Real time object tracking
is often used for surveillance purposes. However it can also be used for instance
for creation of automatic target sentry turrets or as a base information for control
algorithm of camera gimbal mounted to the UAV. Such applications could be
applied in a variety of environments like critical infrastructure protection or for
national border security.
Information acquired by tracking the detected objects can be also used for
higher level algorithms like object and activity recognition. There is a variety of
object recognition applications e.g. car owner recognition based on car plates,
person recognition based on facial features, etc. Regardless the application object
recognition is an essential task for complex systems where a certain amount of
system autonomy is required. However classification large number of features is
time consuming. In order to reduce the number of features often data dimensionality
reduction algorithms like FLD or PCA are used.
Computer vision system combining detection, tracking and recognition
mounted in gimbal under UAV can be successfully applied to various important
tasks like monitoring watercourses or critical infrastructure like pumping gas stations
or high-voltage lines.
Another important challenge is how to track objects in rapid disturbance conditions.
For instance the changes a helicopter position and orientation during the
time of disturbance result in losing tracked object from the field of view. Due to
this fact the helicopter control system uses not only the visual information. The
essence of the potential solution is to compute the object position only in such
time intervals when the object is in the center of the image. It allows the camera
head regulators to compensate change of the camera position and set the camera
towards the object. The solution could comprise of turning the camera head towards
the trucked object in horizontal and vertical planes.