## 4 Experiments
### Quantitative Comparisons
#### 4.2.3 Attribute-Based Performance  
To thoroughly evaluate the performance of the CFSA tracker under various challenging scenarios, we analyze the performance based on the above-mentioned 11 different attributes on OOTB and OTB-100 datasets. Tables 1 and 2 show the precision scores on the OOTB dataset at the center location error threshold = 20 pixels and the success scores of AUC, respectively. The results on the OTB-100 dataset are shown in Tables 3 and 4. The row headers indicate the attributes and its number of image sequences. As shown in these tables, CFSA tracker performs better than other trackers in 9 of 11 attributes and the CFSA tracker exhibits significant improvements in handing scale variation, deformation and in-plane and out-of-plane rotation. Compared with the SAMF tracker which estimates target scale on multiple resolutions of the searching area and achieves prior performance, the performance of CFSA tracker on OOTB dataset outperforms SAMF by 1.8% in the precision plot and 2.5% in the success plot in handing scale variations. This is mainly benefit from the usage of water flow driven MBD algorithm and the proposed scale estimation method, which improved the accuracy of estimated scale of the target object. Moreover, we can observe that the CFSA tracker can well deal with deformation and rotation in two benchmark datasets. This is mainly due to the generated candidate proposals are based on the grouped edge information in an image and the HOG and color naming features are invariant to local small deformation and rotation. However, as shown in Table 2, the SAMF tracker performs better than the proposed CFSA tracker in the case of background  
Figure 5: Precision and success plots over OOTB benchmark dataset using (**a**) temporal robustness evaluation and (**b**) spacial robustness evaluation. The performance score for each tracker is shown in the legend. clutter, the main reason might be that the tracking performance decreases as the difficulty of generating high-quality proposals and estimating the scale of the target object increases. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **CFSA** & **SAMF** & **DSST** & **KCF** & **CSK** & **SCM** & **MIL** & **ASLA** & **TLD** & **Struck** \\ \hline BC-21 & **0.734** & 0.715 & 0.674 & 0.666 & 0.585 & 0.578 & 0.456 & 0.496 & 0.428 & 0.585 \\ DEF-19 & **0.901** & 0.811 & 0.657 & 0.758 & 0.476 & 0.586 & 0.455 & 0.445 & 0.512 & 0.521 \\ FM-17 & 0.648 & **0.657** & 0.513 & 0.611 & 0.381 & 0.333 & 0.396 & 0.253 & 0.551 & 0.604 \\ IV-25 & **0.788** & 0.716 & 0.73 & 0.662 & 0.481 & 0.594 & 0.349 & 0.517 & 0.537 & 0.558 \\ LR-4 & **0.613** & 0.523 & 0.497 & 0.427 & 0.411 & 0.305 & 0.171 & 0.156 & 0.349 & 0.545 \\ MB-12 & **0.637** & 0.632 & 0.544 & 0.619 & 0.342 & 0.339 & 0.357 & 0.278 & 0.518 & 0.551 \\ OCC-29 & **0.893** & 0.868 & 0.706 & 0.692 & 0.5 & 0.64 & 0.427 & 0.46 & 0.563 & 0.564 \\ IPR-31 & **0.803** & 0.735 & 0.768 & 0.727 & 0.547 & 0.597 & 0.453 & 0.511 & 0.584 & 0.617 \\ OPR-39 & **0.826** & 0.788 & 0.735 & 0.704 & 0.54 & 0.618 & 0.466 & 0.518 & 0.596 & 0.597 \\ OV-6 & **0.735** & 0.664 & 0.511 & 0.637 & 0.379 & 0.429 & 0.393 & 0.333 & 0.576 & 0.539 \\ SV-28 & **0.771** & 0.753 & 0.738 & 0.658 & 0.503 & 0.672 & 0.471 & 0.552 & 0.606 & 0.639 \\ All & **0.841** & 0.801 & 0.739 & 0.711 & 0.545 & 0.649 & 0.475 & 0.532 & 0.608 & 0.656 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The precision scores of CFSA tracker and nine other trackers at the center location error threshold = 20 pixels on the OOTB dataset. Bold and underline values indicate best and second performances, respectively.