## 4 Experiments
### Quantitative Comparisons
#### 4.2.4 Components Analysis  
The CFSA tracker consists of three modules: the modified EdgeBoxes (EB), the water flow driven MBD based scale estimation (SE) and PSR based model updating (UP). The CFSA tracker is compared with five other trackers, which are implemented by combining one of the components with multiply features (HOG and color naming) based KCF tracker, including multiply features based KCF tracker MFKCF, EB based tracker MFKCF-EB, SE based tracker MFKCF-SE, UP based tracker MFKCF-UP and EB and SE based tracker MFKCF-EB-SE. These trackers are evaluated on the OTB-100 dataset with OPE. The contribution of each component to the performance of the CFSA tracker is shown in Figure 6. We can observe that the trackers MFKCF-EB, MFKCF-SE, and MFKCF-UP all show a significant drop in both the precision plots and the success plots compared to the CFSA tracker. However, the MFKCF-EB-SE tracker, which combines EB and SE, greatly improves the overall performance of the MFKCF tracker, mainly due to the complementary of each component. Specifically, the EBT tracker tracks the target object with re-ranked high-quality proposals generated by the EdgeBoxes component and achieves promising results on OOTB dataset with 84.8% in the precision plot and 58.1% in the success plot. The EBT tracker outperforms CFSA tracker by 0.7% in the precision plot while the CFSA tracker outperforms EBT tracker by 3.6% in the success plot. This mainly benefits from the combination of locating target object with high-quality proposals based correlation filters and estimating target scale with the proposed scale estimation method.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **CFSA** & **SAMF** & **DSST** & **KCF** & **CSK** & **SCM** & **MIL** & **ASLA** & **TLD** & **Struck** \\ \hline BC-31 & **0.544** & 0.542 & 0.519 & 0.506 & 0.423 & 0.474 & 0.347 & 0.435 & 0.358 & 0.437 \\ DEF-44 & **0.549** & 0.497 & 0.419 & 0.431 & 0.34 & 0.404 & 0.351 & 0.367 & 0.34 & 0.388 \\ FM-38 & **0.541** & 0.536 & 0.441 & 0.46 & 0.339 & 0.304 & 0.299 & 0.25 & 0.434 & 0.479 \\ IV-37 & **0.588** & 0.542 & 0.561 & 0.475 & 0.371 & 0.493 & 0.29 & 0.443 & 0.415 & 0.427 \\ LR-9 & **0.485** & 0.456 & 0.37 & 0.29 & 0.224 & 0.478 & 0.248 & 0.47 & 0.346 & 0.313 \\ MB-28 & 0.534 & **0.538** & 0.463 & 0.459 & 0.323 & 0.282 & 0.258 & 0.245 & 0.436 & 0.479 \\ OCC-49 & **0.55** & 0.541 & 0.449 & 0.443 & 0.335 & 0.435 & 0.328 & 0.384 & 0.368 & 0.398 \\ IPR-52 & **0.567** & 0.527 & 0.52 & 0.47 & 0.385 & 0.417 & 0.348 & 0.406 & 0.433 & 0.459 \\ OPR-63 & **0.562** & 0.534 & 0.47 & 0.45 & 0.356 & 0.433 & 0.351 & 0.411 & 0.39 & 0.428 \\ OV-14 & 0.507 & **0.514** & 0.366 & 0.404 & 0.267 & 0.352 & 0.353 & 0.308 & 0.353 & 0.377 \\ SV-64 & **0.511** & 0.504 & 0.466 & 0.396 & 0.322 & 0.433 & 0.313 & 0.412 & 0.387 & 0.403 \\ All & **0.576** & 0.559 & 0.512 & 0.478 & 0.386 & 0.45 & 0.335 & 0.413 & 0.427 & 0.466 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The success plot’s AUC scores of CFSA tracker and nine other state-of-the-art trackers on the OTB-100 dataset. Bold and underline values indicate best and second performances, respectively.  
Figure 6: Comparison of three components. Precision and success plots on OTB-100 benchmark dataset using OPE are shown.