# rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking
## 5 Experimental Results
### Overall Performance  
In Figures 5 and 6, we depict the precision rate curve and success rate curve of the proposed algorithm and other compared state-of-the-art algorithms using OPE on OTB2013 and OTB2015 datasets. Figures 5(a) and 6(a) show the smooth curve of the distance precision rate when the location error threshold grows from 0 to 50 pixels. Figures 5(b) and 6(b) show the curve of the overlap success rate as the overlap threshold increases from 0 to 1. Meanwhile, the data at a predetermined threshold is displayed in the figure. The proposed method exhibits almost the best performance in both evaluation metrics with the increment of the threshold. Table 1 shows the distance precision rate at a threshold of 20 pixels, overlap success rate at a threshold of 0.5 intersection over union (IOU), and average speed of 10 trackers evaluated on OTB2013. In particular, to accurately evaluate the complexity of these algorithms, the average speed of all trackers was tested on the same hardware environment. The proposed tracker achieves state-of-the-art performance at real-time speed that is superior to many other methods in terms of accuracy. Compared to the baseline Staple, the distance precision rate increases by 6.6%, the overlap success rate increases by 4.9%, and the average center location error increases from 32 to 18.9 pixels according to the results. The overall computational complexity of the proposed algorithm is \(O(nlogn)+O(m)+O(p)+O(q)\). The first part \(O(n\log n)\) comes from the correlation filter estimation and \(n\) represents the pixels of features which are extracted from the search area. The second part \(O(m)\) comes from the color histogram estimation, where \(m\) means the search area size. The third part \(O(p)\) represents scale estimation, where \(p\) is characteristic of the pixels of features in scale pool \(S\). The final \(O(q)\) is representative of the part of model update. (Normally, \(q>p>m\)). Table 2 shows the distance precision rate and overlap success rate of eight trackers evaluated on OTB2015. Since the speed of deep learning-based algorithms depends largely on the hardware facilities such as the GPU, only the accuracy of these trackers is depicted. The proposed method performs better than these state-of-the-art trackers, including deep learning-based algorithms. Moreover, compared to the baseline, the distance precision rate and overlap success rate of our tracker increase by 3.4% and 7.6%, respectively. Figure 5: Precision plots and success plots of one-pass evaluation (OPE) on OTB2013 datasets. Performance within the black box is shown enlarged under the corresponding curve. Figure 6: Precision plots and success plots of OPE on OTB2015 datasets. Expanded performance of the black box is shown under the corresponding curve. Although the difference in distance precision rate and overlap success rate compared to SRDCF and LCT is not obvious in Figure 5, the proposed tracker can achieve better performance at a speed of over two times faster than those methods. Practically, it is desirable for a tracker to provide a better balance between trade-off between efficiency and accuracy. Distance precision and overlap success rate are shown for 11 different attributes in Tables 3-6. The cells in the first row of Tables 3-6 are abbreviations of attributes in the OTB dataset, and the number of videos that belong to each attribute is in parentheses. The first-ranked tracker is marked in bold and the second-ranked tracker is marked in italics. In Table 3, our tracker ranks first in seven attributes; except for the motion blur attribute, the remaining three attributes are within 4% of the first-ranked tracker. Compared to the baseline Staple, our performance is improved in all attributes, especially in these five attributes: deformation (11.1%), illumination variation (11.6%), occlusion (10.6%), out of view (12.6%), and scale variation (10.1%). In Table 4, our tracker ranks first in three attributes and second in five attributes.