A visual tracking algorithm based on the anisotropic Gaussian distribution was proposed, in order to improve the tracking performance of the effective convolution operation algorithm (ECOhc) with traditional features. The anisotropic Gaussian function with different horizontal and vertical bandwidths is constructed according to the shape ratio of different objects and then the function is used to train the tracker so as to predict the position and improve the tracking accuracy. The color histogram features of the object are extracted to track and predict the new position. And then the two predicted positions are weighted fused at the decision layer, which further improves the tracking accuracy. The algorithm was evaluated on the OTB-100 and VOT2016 datasets. The average distance accuracy and overlap rate of the proposed algorithm in OTB-100 were 89.6% and 83.7%, which were 4.67% and 6.62% higher than that of the ECOhc method, respectively. The expected average overlap rate in VOT2016 was 33.3%, which was 3.42% higher than that of the ECOhc method. The proposed algorithm can effectively improve the accuracy of tracking, and it has good robustness when encountering interferences such as occlusion, illumination variation and deformation.
Fig.1Overall framework diagram of proposed algorithm
Fig.2Comparison of two target feature distribution with different aspect ratios
Fig.3Aspect ratios of tracking objects on dataset OTB-100
Fig.4Average distance precision under different position fusion factors
带宽因子组合
$\overline{\rm{DP}} $/ %
$\overline{\rm{OP}} $/ %
1）注：第1、2名分别用粗体字和下划线标出
（1/15，1/12）
88.31）
83.2
（1/15，1/11）
88.0
83.1
（1/15，1/10）
89.6
83.7
（1/14，1/13）
87.3
82.0
Tab.1Comparison results of tracking algorithms with different bandwidths on dataset OTB-100
跟踪算法
$\overline{\rm{CLE}} $/pixel
$\overline{\rm{DP}} $/%
$\overline{\rm{OP}} $/%
V/（帧·s?1）
1）注：第1、2名分别用粗体字和下划线标出
ECOhc
22.7
85.6
78.5
60.0
ECOhc_sig
19.5
86.7
79.8
69.0
ECOhc_his
18.41）
86.9
82.0
56.1
本研究算法
15.9
89.6
83.7
42.6
Tab.2Comparison results of different algorithms on dataset OTB-100
Fig.5Precision and success plots of different algorithms for four different videos
Fig.6Distance precision and success rates of ten algorithms on dataset OTB-100
跟踪算法
$\overline{\rm{DP}} $ / %
$\overline{\rm{AUC}} $ / %
1）注：第1、2名分别用粗体字和下划线标出
本研究算法
89.61）
66.5
SiamRPN
85.1
63.7
DaSiamRPN
88.0
65.8
SiamFC+CIR
85.0
64.0
SiamRPN+CIR
86.0
67.0
C-RPN
?
66.3
LDES
76.0
63.4
DAT
89.5
66.8
Tab.3Comparison results of different algorithms on dataset OTB-100 in recent years
Fig.7Tracking results of ten algorithms for typical video sequences
跟踪算法
EAO
A
R
1）注：第1、2名分别用粗体字和下划线标出
本研究算法
0.333
0.53
1.03
ECO
0.3731）
0.54
0.72
C-COT
0.331
0.52
0.85
ECOhc
0.322
0.53
1.08
CSR-DCF
0.338
0.51
0.85
Staple
0.295
0.54
1.35
D_SRDCF
0.274
0.52
1.23
Tab.4Comparison results of different algorithms on dataset VOT2016
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