03-09-2012, 12:26 PM
INTRODUCING NEW PARAMETERS TO COMPARE THE ACCURACY AND RELIABILITY OF MEAN-SHIFT BASED TRACKING ALGORITHMS
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ABSTRACT
Mean shift algorithms are among the most functional tracking methods which are accurate and have
almost simple computation. Different versions of this algorithm are developed which are differ in template
updating and their window sizes. To measure the reliability and accuracy of these methods one should
normally rely on visual results or number of iteration. In this paper we introduce two new parameters
which can be used to compare the algorithms especially when their results are close to each other.
INTRODUCTION
Real time object tracking is an important practical application of computer vision with a wide
variety of usages in different subjects such as surveillance systems, road traffics, weather
forecasting, machine and human interfaces, video compression, astronomy, and a lot of military
issues [1-10]. Among different tracking algorithms, mean shift based methods have their special
places because of their simplicity in computation while have good accuracies. Updating the target
model and making the tracking window size dynamic are two important factors which have made
authors develop versions of mean shift tracking algorithms [11-14]. To have a good judgment
about the accuracy and reliability of different methods we should use acceptable robust
parameters. The comparisons are often done by visual observation while considering the number
of iteration in the process. Normally the lesser iteration indicates on higher speed of the tracker,
although sometimes it is not satisfactory agument. For example the tracker may loss the target
during its process, causing its window be fix in one position which can result in unreliable
iteration number. To cope with these kind of problems, one solution is to make and use of more
related comparison parametrs.
BASIC MEAN-SHIFT (MS) TRACKING ALGORITHM
We use the basis of MS algorithm to introduce our parameters. Target features and way of
modelling it, target candidates and geometric relations between target model and candidate are
the main subject of the algorithm.
Target features and modelling
In MS tracking algorithm the target feature is the colour PDF1 of target locale which is shown by
q in colour space. The probable place of target with center of y in next frame contains target
candidate. To make target candidate model the PDF of its locale shown by p(y) is used. Both
PDFs (target and candidate), contains a good approximation of intrinsic feature of target. As one
of the trackers evaluation parameters is their processing time, normally to reduce that only some
of histogram bars are selected and used.
CONCLUSION
Number of iteration in each frame and visual observation are basic parameters to compare the
performances of Mean-Shift based tracking methods. In some cases such as when the results are
high correlated it is hard to compare the algorithms by mentioned parameters. In this paper we
presented two new parameters in order to compare the accuracy and reliability of MS based
trackers. The result of examined video tests show even in the case of closeness of tracker outputs
it is possible to have a good assessment regarding algorithms performances using proposed
parameters.