Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (12): 3518-3528.doi: 10.13229/j.cnki.jdxbgxb.20220166

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Anchor⁃free target tracking algorithm based on multiple attention mechanism

Jing-hong LIU1(),An-ping DENG1,2,Qi-qi CHEN1,2,Jia-qi PENG3,Yu-jia ZUO1   

  1. 1.Changchun Institute of Optics，Fine Mechanics and Physics，Chinese Academy of Sciences，Changchun 130033，China
    2.University of Chinese of Sciences，Beijing 100039，China
    3.The First Military Representative Office of the Military Representative Bureau of the Army Equipment Department of the Chinese People's Liberation Army in Shenyang and in Changchun，Changchun 130022，China
  • Received:2022-02-21 Online:2023-12-01 Published:2024-01-12

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Abstract:

Siamese network based trackers have two branches which are independent of each other and lack of infor-mation interaction. So it cannot accurately and robust tracking under the challenges of target occlusion and similar object. To solve this problem， an anchor-free target tracking algorithm based on multiple attention mechanism was proposed. Multiple attention mechanism was used to encode the target template and search area features. After improving the feature significance through self-attention mechanism， mutual attention mechanism was used to aggregate the feature interaction between target template and search area， which strengthens this algorithm's discri-mination ability between target and background. At the same time， the anchor-free mechanism was used to complete the end-to-end visual target tracking task pixel by pixel， avoiding the disadvantages of human intervention caused by the anchor frame mechanism. Extensive experiments are conducted on many challenging benchmarks like OTB50， OTB100 and GOT-10K. These results show the anchor-free target tracking algorithm based on multiple attention mechanism proposed has strong robustness against the challenges of target occlusion and similar object， and effectively improves the precision rate and success rate of the tracking algorithm.

Key words: computer vision, object tracking, attention mechanism, anchor-free

CLC Number: 

  • TP391.4

Fig.1

The proposed algorithm frame diagram"

Fig.2

Spatial attention mechanism map"

Fig.3

Channel attention mechanism map"

Fig.4

Cross attention mechanism map"

Fig.5

OTB50 Comparison chart"

Fig.6

Comparison of accuracy and success rate of 4 attributes on OTB50 dataset"

Fig.7

Comparison of heat map"

Fig.8

Comparison chart of qualitative results"

Table 1

Experimental comparison results of different algorithms on the GOT-10K dataset"

指标SiamFCSiamRPNSiamRPN ++ATOMSiamCAR本文
AO0.3740.4630.5160.5560.5690.576
SR0.500.4040.5490.6200.6340.6700.672
SR0.750.1440.2530.3340.4020.4150.439
FPS25.87426211817

Table 2

Comparative experiment on operational efficiency"

算法FLOPsParamsFPS
SiamCAR83.2G91.9M18
本文84.5G93.9M17
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