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1 Bhattacharyya Coefficient 0.9
0.3 Initial location
Convergence point 0.2
Y 40 0 20 −20 −40 X Figure 3: Values of the Bhattacharyya coe cient corresponding to the marked region (81 81 pixels) in
frame 105 from Figure 1. The surface is asymmetric,
due to the player colors that are similar to the target.
Four mean shift iterations were necessary for the algorithm to converge from the initial location (circle).
To demonstrate the e ciency of our approach, Figure 3 presents the surface obtained by computing the
Bhattacharyya coe cient for the rectangle marked in
Figure 1, frame 105. The target model (the selected
elliptical region in frame 30) has been compared with
the target candidates obtained by sweeping the elliptical region in frame 105 inside the rectangle. While most
of the tracking approaches based on regions 3, 14, 21] Figure 1: Football sequence: Tracking the player no.
75 with initial window of 71 53 pixels. The frames 30,
75, 105, 140, and 150 are shown.
5 must perform an exhaustive search in the rectangle to
nd the maximum, our algorithm converged in four iterations as shown in Figure 3. Note that since the basin
of attraction of the mode covers the entire window, the
correct location of the target would have been reached
also from farther initial points. An optimized computation of the exhaustive search of the mode 13] has a
much larger arithmetic complexity, depending on the
chosen search area.
The new method has been applied to track people on
subway platforms. The camera being xed, additional
geometric constraints and also background subtraction
can be exploited to improve the tracking process. The
following sequences, however, have been processed with
the algorithm unchanged.
A rst example is shown in Figure 4, demonstrating
the capability of the tracker to adapt to scale changes.
The sequence has 187 frames of 320 240 pixels each
and the initial normalization constants were (hx hy ) =
Figure 5 presents six frames from a 2 minute sequence showing the tracking of a pers...
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