Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (3): 51-57.doi: 10.6040/j.issn.1672-3961.0.2019.414

• Machine Learning & Data Mining • Previous Articles     Next Articles

Adaptive fusion target tracking based on joint detection

Baocheng LIU(),Yan PIAO*(),Xuemei SONG   

  1. College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130012, Jilin, China
  • Received:2019-07-22 Online:2020-06-01 Published:2020-06-16
  • Contact: Yan PIAO E-mail:719741840@qq.com;piaoyan@cust.edu.cn
  • Supported by:
    吉林省科技支撑资助项目(20180201091GX);吉林省科技创新中心资助项目(20180623039TC)

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Abstract:

Due to the interference of various factors in the complex situation of reality, the trackers had some problems such as model drift and tracking failure. An adaptive fusion target tracking based on joint detection algorithm was proposed to improve the robustness and accuracy of the tracker. The deep and shallow convolutional features acted on the correlation filters separately to obtain response scores according to their respective advantages, and adaptively fused the response scores of different convolutional features by minimizing the loss. Then it combined with the location detection method to judge the validity and authenticity of the predicted location, so as to get the optimal target tracking results. A large number of tests were done in two open databases: OTB-2015 and VOT-2017. The experimental results showed that the proposed method was 10% more robust and 3.9% more accurate than the LSART algorithm. It also had excellent performance for occlusion and scale variation.

Key words: machine vision, target tracking, adaptive fusion, convolutional feature, correlation filtering

CLC Number: 

  • TP391.41

Fig.1

Block diagram of tracking based on correlation filters"

Fig.2

The overall block diagram of the proposed approach"

Fig.3

Tracking results of five video sequences"

Table 1

Comparison of the proposed method with the trackers of OTB-2015"

方法 精确率/% 成功率/%
Ours 92.4 71.0
ECO 91.0 70.0
C-COT 90.9 69.0
MDNet 89.9 68.5
TCNN 88.4 66.1
DeepSRDCF 85.1 64.3
STRCF 83.5 65.1
BACF 81.3 62.2
SRDCF 78.9 60.5
SiameseFC 77.3 58.2

Table 2

Comparisons of the proposed method with the trackers of VOT-2017"

方法 平均重叠期望 准确性 鲁棒性
Ours 0.341 0.512 0.196
LSART 0.323 0.493 0.218
CFWCR 0.303 0.484 0.267
CFCF 0.286 0.509 0.281
ECO 0.280 0.483 0.276
Gnet 0.274 0.502 0.276
MCCT 0.270 0.525 0.323
C-COT 0.267 0.494 0.318
CSRDCF 0.256 0.491 0.356
SiamDCF 0.249 0.500 0.473
MCPF 0.248 0.510 0.427
UPDT 0.378 0.536 0.184
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