Research on Target Tracking Algorithm Based on Similarity Feature Estimation

Bo Zhang; Gang Liu

doi:10.3788/LOP57.241005

Laser & Optoelectronics Progress, Volume. 57, Issue 24, 241005(2020)

Research on Target Tracking Algorithm Based on Similarity Feature Estimation

Bo Zhang^1、* and Gang Liu^1,2

Author Affiliations

¹College of Information Science and Engineering, Changsha Normal University, Changsha, Hunan 410100, China

²Physical Science and Electronics, Central South University, Changsha, Hunan 410083, China

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Abstract Get PDF(in Chinese)

Figures & Tables(10)

Fig. 1. Neighborhood method

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Fig. 2. Specific method for selecting specific search area

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Fig. 3. Tracking results of target tracking algorithm based on similarity feature estimation

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Fig. 4. Performance comparison of different target tracking algorithms, under the experimental scene of enlarged target size

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Fig. 5. Performance comparison of different target tracking algorithms, under the experimental scene of reduced target size

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Fig. 6. SNR comparison of output results with different target tracking algorithms

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Table 1. Specific configuration data of the experimental environment
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Table 1. Specific configuration data of the experimental environment
No. Configurationtype Specificconfiguration Configurationdata
1 Hardware part CPU Intel Core 2
Frequency/GHz 1.2
RAM/G 2
2 Software part Operating system XP
Development tools 2015 VS platform

Table 2. Specific configuration data of video coding in the experiment

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Table 2. Specific configuration data of video coding in the experiment

No.	Configuration type	Configuration data
1	Coding class	High class
2	Image tracking size	4CIF、CIF
3	Encoding bandwidth/k	2048、1024、768、384
4	Coding format	4MPEG
5	Reference code	XVID reference software
6	Code specific bit rate	1024
7	Encode the specificframe rate/(frame/s)	25

Table 3. Parameter specific configuration data
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Table 3. Parameter specific configuration data
No. Configuration type Configuration data
1 Color space configuration HSV space
2 Component configuration H component
3 Quantization treatment series 32
4 Kernel function Epanachnekov

Table 4. Time-consuming comparison of target tracking process with different algorithms

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Table 4. Time-consuming comparison of target tracking process with different algorithms

Scenario	Enlarge the target sizeexperiment scene			Reduce the target size of theexperimental scene
Track target number	1	3	5	1	3	5
Target tracking algorithm based onsalient features/ns	623	789	817	626	749	860
Algorithm based on fusionfeatures/ns	571	577	634	524	581	612
Algorithm based on backgroundweighting/ns	597	682	701	582	654	693
Algorithm based on convolutionalnetwork/ns	412	421	430	461	487	496
Target tracking algorithm based on kernelcorrelation filtering of dual feature model/ns	532	597	631	624	636	675
Algorithm based on similarityfeature estimation/ns	245	344	435	267	355	430

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Bo Zhang, Gang Liu. Research on Target Tracking Algorithm Based on Similarity Feature Estimation[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241005

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