International Core Journal of Engineering 2020-26 | Page 80

Fig. 6. Comparison of tracking results in experiment 3.
The comparison of the quantization steps of the
histogram and the changes of average time-consumption
during the algorithm’s running was given in Fig. 7. It can be
clearly seen from Fig. 7, concerning the size of the target is
very small in the initial state, the time consumption with
quantization step of 32 in the proposed algorithm is
obviously lower than that for the quantization step of 256 in
the traditional algorithm. As the target moves from distant to
near, the quantization step adopted in the proposed algorithm
is increased as the target size increases, thereby, leading to
the increase of the time consumption. When the target moves
closer, the quantization step reaches 256, which is consistent
with that in the traditional algorithm, and the
time-consumption of the proposed algorithm tends to be
consistent with that of the traditional algorithm. The
comparisons of time-consumptions for the average frame
processing of traditional meanshift and the proposed
algorithm were depicted in Fig. 8, showing that the operating
efficiency of the improved algorithm had been improved by
about 10%.
under different experimental envrionment demonstrate the
improved efficiency of the proposed algorithm.
(ms)
Proposed
algorithm
Conventional
algorithm
Experiment 1
Experiment 2
Experiment 3
Fig. 8. Comparisons of average per-frame time consumptions in the above
experiments.
A CKNOWLEDGEMENTS
The research work described in this paper is supported by
Anhui provincial natural science research project of colleges
and Universities (No. KJ2017A012) and the open project of
Key lab of Optc-electronic Information Acquisition and
Manipulation Ministry of Education, Anhui University
(OEIAM201401). The authors would like to thank all
members of Intelligent Video Research Group from IIP-HCI
lab of Anhui University for their valuable suggestions and
assistance in preparing this paper.
(ms)
Number of
quantization steps
Proposed algorithm’s
average time consuming
Conventional algorithm’s
average time consuming
R EFERENCES
[1]
Fig. 7. Comparisons of quantization steps and relative time consumptions
in experiment 3.
[2]
IV. C ONCLUSION
The target tracking algorithm based on traditional
meanshift cannot dynamically adjust the quantization steps
as the target size changes. An improved algorithm based on
the traditional meanshift has been proposed in this work.
Once the target size changes, the proposed algorithm can
adaptively adjust the quantization step and use DTW to carry
out model matching in order to increase the operating
efficiency and computational flexibility of the tracking
algorithm. The idea of the proposed algorithm can be easily
understood and the improvement is not complicated,
enabling the application feasibility. The experimental results
[3]
[4]
[5]
[6]
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