International Core Journal of Engineering 2020-26 | Page 79

A video clip recorded on a campus road was used in
experiment 1 where a red vehicle moved from the near to the
distant. Fig. 2 (A1)-(A4) correspond to the tracking results
based on the traditional meanshift algorithm, while Fig. 2
(B1)-(B4) correspond to the results of the proposed
algorithm. The moving target is located in the yellow frame.
It can be clearly seen that the traditional meanshift algorithm
cannot adaptively change the tracking window as the target
size changes. As the target moves deeper along the depth of
field, the traditional meanshift algorithm’s ability of
depicting the target with histogram features is constantly
weakened, thereby, providing only a very coarse target
position. From the results of the proposed algorithm, it can
be seen that the tracking window changes adaptively with the
target, and the quantization step decreases with the decrease
of the target size, leading to a more accurate tracking result.
A clip of sport video was used in experiment 2 where
there was a skier who quickly swept the camera. From Fig.
3(A1)-(A4), it can be clearly seen that due to the complex
background, the traditional algorithm loses the ability to
depict the target when the target becomes smaller, resulting
in the loss of the target. However the proposed algorithm can
timely update the target size and adjust the quantization steps
in time to maintain the validity of each frame of candidate
model, thereby, achieving more accurate tracking even in the
complex background of rapid movement.
Fig. 3. Comparison of tracking results in experiment 2.
In addition to the visual tracking results, the efficiency
improvement of the proposed algorithm was also examined.
Fig. 4 shows the changes of the time consumptions for frame
processing caused by the automatic adjustment of the
quantization steps in experiment 1 and experiment 2 (the
horizontal axis stands for the frame number, same
hereinafter). It can be seen that the time consumption for
frame processing is reduced with the automatic reduction of
the quantization steps. Fig. 5 shows the comparison of
average time-consumption of traditional meanshift and the
proposed method in experiment 1 and experiment 2. As far
as the operating efficiency is concerned, the improved
algorithm is basically consistent with the traditional
algorithm when the quantization step is not adaptively
reduced, but when the quantization step is automatically
reduced, the time consumption has significantly been
reduced.
Average time consuming
⴨ሩ㙇ᰦ
Conventional algorithm
Ր㔏㇇⌅
12
10
Number
of quantization steps
䟿ॆ䱦ᮠ
256bins
Average time consuming
⴨ሩ㙇ᰦ
8
6
4
2
1 2 3 4 5 6 7 8 9 10 11
128bins
64bins
(A)
64bins
892 898 926 934 938 940 942 956 960 980 982 24 28 44 48 52 56 60 68 72 76 77
(A) (B)
8
6
4
2
0
0
128bins
Conventional algorithm
Ր㔏㇇⌅
10
256bins
Proposed algorithm
ᵜ᮷㇇⌅
Proposed algorithm
ᵜ᮷㇇⌅
Number of quantization steps
䟿ॆ䱦ᮠ
The moving targets in experiments 1 and 2 moved from
the near to the distant along the depth of field. In order to
further verify the validity of our algorithm, a video clip with
object moving from the distant to the near was used. The
results were shown in Fig. 6. In the tracking results of the
traditional meanshift shown in Fig. 6 (A1)-(A4), the tracking
algorithm focus on the local part of the vehicle due to the
fact that the size of the target in the initial state is small and
hence cannot be adaptively adjusted In the tracking results of
the proposed algorithm shown in Fig. 6 (B1)-(B4), the
algorithm can adaptively change the quantization step and
target size, leading to better tracking result.
1
2
3
4
5
6
7
8
9 10 11
(B)
Fig. 5. Comparison of running time-consumptions before and after
algorithm improvement. (A) Comparison of experiment 1; (B) Comparison
of experiment 2.
Fig. 4. Comparison of changes of quantization steps and average
time-consumptions in experiment 1 and experiment 2. (A) Result of
experiment 1; (B) Result of experiment 2.
57