# Minimum Barrier Distance-Based Object Descriptor for Visual Tracking
## 4 Experimental Results
### Quantitative Evaluation
#### 4.3.1 Evaluation on OTB-100
We can see from the illustration that our algorithm outperformed on deformation compared with other trackers; especially, our tracker was better than the second tracker CNN-SVM [8] by 2% and 1.6% in precision and success rates on DEF, respectively. At the same time, our tracker achieved performance gains of 5.7% in the PR score and 3.9% in the SR  
Figure 5: The evaluation results on OTB-100. The left shows precision plots, and the right is the success plots of one-pass evaluation (OPE). score over SOWP [10] on FM. Compared with SOWP adopting the weighted descriptor, it shows that the MBD-based weighted patches performed more robustly with the fluctuation of node values. Figure 6: _Cont._  
The results in Figure 6 show that the hard positive sample generated by training the hard positive transformation network in SINT++ [2] was advantageous for OV, while our methods outperformed SINT++ in the other seven attributes. The latest MemTrack [51] obtained the best results on most of the attributes due to the superiority of deep networks. The MUSTer [20] tracker achieved better results over ours for BC by exploiting both the short-term and long-term systems to process the image with the target being tracked, but its overall performance was not so robust as ours. ACFN [18] achieved better results for SV in the success plots than our method, but our algorithm outperformed against the ACFN on the other attributes including OCC, FM and BC. This shows that our proposed MBD-based weighted descriptor could locate the target object in a complex background even without strong features. These experiments validate the effectiveness of the proposed algorithm. We compared our tracker with real-time part-based visual tracking (RPVT) [43] with dynamic weighting. RPVT tested its method on 16 selected challenging tracking sequences in OTB-50 [1] to show its ability for handling occlusion, scale and appearance changes. We have also run our method on the same sequences and adopted the average center location error and average overlap rate the same as in that paper, and the results are shown in Tables 3 and 4. In general, RPVT obtained better results than ours for these 16 tracking sequences. However, our tracker performed better on the most of the other 34 sequences in Table 5. This evinces that RPVT was better for handling occlusion, scale and appearance changes, while our tracker was good at the challenges including fast motion, background clutter and deformation. Figure 6: The evaluation results of precision rate (PR) and success rate (SR) on eight attributes. +++ ==ERROR: No output for this page== \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline  & **Backshell** & **Bolt** & **Boy** & **CarDark** & **Cross** & **David1** & **David2** & **David3** & **Dog** & **Doll** & **Dodak** & **Fecececec1** & **Faresecec2** & **Fish** & **Fecececec2** & **Fish** & **Fecececec2** & **Fish** & **Fecececec2** & **Fish** & **Fecececec2** & **Fish** & **Fecececec2** & **Fish** & **Fecececec2** & **Fish** & **Fecececec2** \\ \hline RPVT [40] & 6.3 & 8.2 & 3.5 & 3.1 & 7.6 & 8.5 & 10.3 & 16.4 & 4.2 & 8.5 & 10.9 & 10.8 & 5 & 8.7 & 8.5 & 20.1 & 73 \\ Ours & 4.2 & 3.5 & 2.1 & 1.1 & 2.2 & 3.7 & 1.8 & 5.1 & 5.1 & 6.5 & 11.0 & 14.0 & 9.3 & 8.5 & 21.5 & 61.5 & 54.8 \\ \hline
**Jogging** & **Jumping** & **Lemming** & **Lumping** & **Matters** & **Mbyng** & **Motor** & **MountainBike** & **Stating1** & **Staining** & **Soccer** & **Suitway** & **Saw** & **Tiger2** & **Telling** & **Walking** & **Woman** \\ \hline RPVT [43] & 14.6 & 7.0 & 22.4 & 13.6 & 26 & 17.3 & 67.8 & 6.3 & 7.9 & 86.2 & 53.6 & 4.3 & 9 & 12.3 & 10.6 & 5.3 & 4.1 \\ Ours & 4.4 & 3.0 & 7.0 & 6.1 & 86.0 & 4.7 & 148.7 & 8.6 & 21.6 & 4.7 & 61.4 & 3.5 & 11.8 & 11.3 & 12.7 & 3.4 & 4.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average center location error comparison with RPVT [43] on other sequences in OTB-50. The red number is the best result. We also compared our tracker with the occlusion probability mask-based tracker (OPM) [41].