Aerospace and Electronic Systems Magazine September 2016 - 22


Onboard	Visual	Sense	and	Avoid	System	for	Small	Aircraft
Two different kinds of detection strategies are used depending on the background
complexity. The first is for detecting remote aircraft against an
unstructured background (clear
sky or sky with low or medium
contrast clouds), while the second was developed for situations
when the background is structured (terrain or high contrast
sky background). A local edge
density measure classifies the regions of the images and the two
kinds of detections are run on the
different regions. The block diagram of the algorithm is shown
in Figure 5.
In order to reduce the computational cost of the algorithm,
only the preprocessing is run
on the whole frame. After the
preprocessing part, the more
complex algorithms are run on
small windows cut from the image. The interesting locations
are found based on the binary
edge map. The threshold is set
depending on the contrast and
the number of found objects.
Figure 5.
This way the processing system
Aircraft detection algorithm.
can adapt to various lighting
conditions and situations.
In the case of the unstructured background, a two-dimensional convolutional filter optimized to extract the small (2-3 pixels
large) horizontally elongated blobs is applied. Here, detected pixel
groups only larger than two pixels count; the smaller ones are not
considered to be candidate points and are discarded. The false
candidates are filtered out using local features. The details can be
found in [20].
In the structured background scenario another strategy is
needed, as the contrast of the aircraft drops significantly. In this
situation, when the object is not detectable on a still image (intraframe) due to the lack of definite shape, contrast, or color, then its
movement should be detected using an inter-frame approach. Due
to the moving platform, the standard Gaussian Mixture Modeling
cannot be used and the standard frame differentiation technique
is not feasible for the task as well because it is computationally
expensive. In our algorithmic framework, a simplified version of
the frame differentiation is used, where we compensate the camera
ego-motion with shift only, without scaling and rotation. According to our experiments, the shift only approach still leads to acceptable results, because the objects on the image are at a large
distance, therefore they are not enlarging significantly in between
two frames, and also, the frame-rate is high enough to keep the effect of rotation very small [23].
22	

In both cases the center of mass and size of the identified object
is calculated in the image as the output of the process. This information is given to the motion estimation module, and later on the
collision risk is estimated based on that.

COLLISION RISK ESTIMATION BASED ON IMAGE
PROCESSING
The collision risk estimation method is based on [15], which proposes a CPA-based decision about the risk of collision. In the derivation
of following formulae it is assumed that the intruder flies straight
with constant velocity. In an XC , YC , ZC camera frame x; y are the
positions of intruder image centroid and Sx ; Sy are the intruder image
sizes (horizontal/vertical). A pinhole camera model is used which
relates image parameters (x; y; Sx ; Sy) to own aircraft camera focal
length f, intruder position (X; Y; Z) in camera frame, intruder size Rx/y
(horizontal/vertical), intruder relative velocities Vx ; Vy ; Vz in camera
frame, time to collision tTC (defined to go to zero as the two aircrafts
approach each other), miss distances at Z = 0 Xa ; Ya (Z is the axis of
camera frame pointing forward relative to own craft), and relative
miss distances CPA = Xa /Rx or Ya /Ry (called also as closest point of
approach CPA). The basic equations of pinhole camera model are:

IEEE	A&E	SYSTEMS	MAGAZINE	

SEPTEMBER	2016



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