PROCEEDINGS ARTICLE | January 23, 2012
Proc. SPIE. 8301, Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques
KEYWORDS: Video, Cameras, Image segmentation, Target acquisition, Target detection, Data modeling, Imaging systems, Computer simulations, Performance modeling, Light sources and illumination
An aerial multiple camera tracking paradigm needs to not only spot unknown targets and track them, but also needs to
know how to handle target reacquisition as well as target handoff to other cameras in the operating theater. Here we
discuss such a system which is designed to spot unknown targets, track them, segment the useful features and then create
a signature fingerprint for the object so that it can be reacquired or handed off to another camera. The tracking system
spots unknown objects by subtracting background motion from observed motion allowing it to find targets in motion,
even if the camera platform itself is moving. The area of motion is then matched to segmented regions returned by the
EDISON mean shift segmentation tool. Whole segments which have common motion and which are contiguous to each
other are grouped into a master object. Once master objects are formed, we have a tight bound on which to extract
features for the purpose of forming a fingerprint. This is done using color and simple entropy features. These can be
placed into a myriad of different fingerprints. To keep data transmission and storage size low for camera handoff of
targets, we try several different simple techniques. These include Histogram, Spatiogram and Single Gaussian Model.
These are tested by simulating a very large number of target losses in six videos over an interval of 1000 frames each
from the DARPA VIVID video set. Since the fingerprints are very simple, they are not expected to be valid for long
periods of time. As such, we test the shelf life of fingerprints. This is how long a fingerprint is good for when stored
away between target appearances. Shelf life gives us a second metric of goodness and tells us if a fingerprint method
has better accuracy over longer periods. In videos which contain multiple vehicle occlusions and vehicles of highly
similar appearance we obtain a reacquisition rate for automobiles of over 80% using the simple single Gaussian model
compared with the null hypothesis of <20%. Additionally, the performance for fingerprints stays well above the null
hypothesis for as much as 800 frames. Thus, a simple and highly compact single Gaussian model is useful for target
reacquisition. Since the model is agnostic to view point and object size, it is expected to perform as well on a test of
target handoff. Since some of the performance degradation is due to problems with the initial target acquisition and
tracking, the simple Gaussian model may perform even better with an improved initial acquisition technique. Also, since
the model makes no assumption about the object to be tracked, it should be possible to use it to fingerprint a multitude of
objects, not just cars. Further accuracy may be obtained by creating manifolds of objects from multiple samples.
