# Robust Visual Tracking via Patch Descriptor and Structural Local Sparse Representation
## 4 Object Tracking
The precision and success plots in terms of one-pass evaluation (OPE) [26] are provided in Figure 4. In the precision plots, we used the distance precision at a threshold of 20 pixels for ranking, while in the success plots, we used the area under curve (AUC) for ranking. As shown in Figure 4, the precision and success scores of our tracker were 0.580 and 0.463, which were ranked the second and first places, respectively. Compared with the baseline ASLA, our tracker achieved an improvement (4.8% in the precision rate and 2.9% in the success rate). The improvement in performance as compared to the ASLA can be attributed to two aspects: (i) we modeled the appearance of the object by the combination of the patch descriptor and the patch sparse coefficients, which made the tracker more robust to changes of target appearance, because the uses of patch descriptors allowed us to adjust the contribution of each patch in the observation model according to appearance changes; (ii) we designed an outlier-aware template update scheme to alleviate the model drift caused by outlier samples. The VTD and Frag are also the patch-based trackers. Our tracker outperformed the VTD by 4.7% and the Frag by 11.3% in the success plot, respectively. Compared to the CSK and STC, the proposed tracker showed higher precision and success scores. For the deep learning-based tracker, our tracker outperformed the DLT by 4.8% on the success score. ### Attribute-Based Analysis

In this subsection, to further evaluate the performance of our tracker under different challenges, we conducted an attribute-based evaluation on the OTB-2013 benchmark dataset. The dataset video sequences were annotated with 11 attributes including occlusion, illumination variation, scale variation, fast motion, motion blur, deformation, background clutters, out-of-view, out-of-plane rotation, in-plane rotation and low resolution. We reported the precision plots and success plots of different trackers on these 11 attributes in Figure 5, respectively. Figure 4: The precision and success plots of the tracking results on the OTB-2013 benchmark dataset. The legends contain the scores of the center location error with the threshold of 20 pixels and values of the area under curve for all trackers in the precision and success plots, respectively. Note that the color of one curve is determined by the rank of the corresponding trackers, not their names. In the precision plots, as shown in Figure 5, our tracker outperformed the baseline ASLA on 9 of 11 attributes. Specially, in the \"occlusion\", \"motion blur\" and \"scale variation\" attributes, our tracker achieved 9.6%, 8.2% and 7.5% better performance than the ASLA, respectively. Moreover, in terms of success plots, as shown in Figure 6, our tracker also improved the baseline ASLA on 8 attributes. In addition, in the attributes of \"deformation\", \"occlusion\", \"out-of-plane rotation\", \"scale variation\" and \"low resolution\" attributes, our tracker had the best performance among all the evaluation trackers in terms of the success score. Figure 5: Precision plots for different attributes. The legend contains the precision score of each tracker at 20 pixels. ### Evaluation of Template Update Strategy

We further evaluated the proposed outlier-aware template update strategy with comparison to the original template update strategy used in the ASLA [9]. Experimental results on the OTB-2013 benchmark dataset are presented in Figure 7. The \"Ours_Outlier-aware_Update_Strategy\" denotes our tracker using the proposed outlier-aware template update strategy, and the \"Our_ASLA_Update_Strategy\" denotes our tracker using the template update strategy proposed in ASLA. Our baseline tracker was the ASLA. Figure 2:0 Success plots for different attributes. The legend contains the AUC score of each tracker. As shown in Figure 7, the proposed outlier-aware template update strategy improved the tracking performance compared to the template update strategy proposed in the ASLA.