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Table 2 Tracking performance and frame per second (FPS) of the state-of-the-art approaches on OTB-100 benchmark. “-“denotes invalid state; the bold fonts indicate the best results

From: Learning attention for object tracking with adversarial learning network

Algorithms Techinique Code type Precision Success rate FPS
Ours Deep Learning MATLAB & C++ 0.919 0.719 14.8
MUSTer Correlation Filter MATLAB & C++ 0.774 0.575 6.1
DSST Correlation Filter MATLAB & C++ 0.693 0.52 35.5
SiamFC Deep Learning MATLAB & C++ 0.771 0.691 31.2
CCOT Correlation Filter MATLAB & C++ 0.691 0.682 2.6
MDNet Deep Learning MATLAB 0.788 0.678 1.4
SIT Deep Learning MATLAB 0.732 0.575 225.5
PCOM   MATLAB & C++ - - 27.6
KCF Correlation Filter MATLAB 0.690 0.477 124.1
CN   MATLAB & C++    65.4
Struck SVM C++ - - 9.6
TLD Boosting MATLAB & C++ - - 2.7
MIL Boosting C++ - - 31.8