# Robust Visual Tracking via Patch Descriptor and Structural Local Sparse Representation
## 4 Object Tracking
Only the [PERSON], STC and our tracker successfully located the correct object throughout the sequence. The object in the Skating1 sequence (Figure 9c) suffered from frequent non-rigid appearance changes and illumination variations. The Frag, CT and L1 APG lost the object around frame 61, but the VTD and our tracker could survive to the end. **Scale variation:** Figure 10 shows screenshots of three videos in which the objects underwent scale variations. For the CarScale sequence (Figure 10a), we can see that the TLD, VTD, L1 APG, CSK and STC failed to locate the car when it moved closer to the camera. In the Freeman3 sequence (Figure 10b), the scale of the man changed largely. Only the DLT, ASLA and our tracker could reliably track the object at the most frames. Figure 10c shows the tracking results in the Singer1 sequence, where significant scale and illumination variation were noticed. The Frag and L1 APG could not adapt to the scale variation and finally failed to track the object at different time instances (e.g., #88 and #135), and the CT and CSK obtained wrong size of the object (e.g., #236 and # 351). Figure 9: Screenshots of some sampled tracking results, where objects undergo deformations. (**a**) Crossing with deformation and illumination variation; (**b**) David3 with deformation and occlusion; (**c**) Skating1 with deformation and illumination variation. **Rotation:** In the [PERSON] sequence (Figure 11a), the person changed the orientation of his face over time, and the varying illumination also made the tracking harder. The Frag and DLT failed to locate the object (e.g., #62 and #189). For the [PERSON] sequence (Figure 11b), the pose of the man varied slightly. The Frag lose the object (e.g., # 946 and #1030). The Freeman4 sequence (Figure 11c) included rotation and occlusion. It was difficult to handle both of these two challenges. Only our tracker could track the object well. Figure 11: Screenshots of some sampled tracking results, where objects suffer from rotation. (**a**) [PERSON] with rotation and illumination variation; (**b**) Dudek with rotation and scale variation; (**c**) Freeman4 with rotation and occlusion. Figure 10: Screenshots of some sampled tracking results, where objects suffer from significant scale variations. (**a**) CarScale with scale variation and occlusion; (**b**) Freeman3 with scale variation and rotation; (**c**) Singer1 with scale variation and illumination variation. ## 7 Conclusions

In this paper, we presented a tracking method utilizing the patch descriptor and the structural local sparse representation. The novelty of the paper is to design the patch descriptors defined as the proportion of sub-patches, of which the reconstruction error was less than the given threshold, which would distinguish each patch of the target candidate and reflect the degree of corruption or occlusion of the target. In order to effectively reduce model drift caused by noisy updates, we designed an outlier ratio to describe the outlier degree of a target object. When the outlier ratio was larger than the threshold, we stopped updating the template. Both the quantitative and qualitative evaluations on the OTB-2013 benchmark dataset have been done to verify the effectiveness of the proposed algorithm. [PERSON] designed the presented tracking algorithm and wrote the paper; [PERSON] analyzed the experimental data; [PERSON] revised the paper; and [PERSON] supervised the work. This work is supported by the key R&D project of Xiangxi Tujia&Miao Autonomous Prefecture, China (No. 2018 GX2003). **Acknowledgments:** The authors would like to thank the editors and the anonymous referees for their valuable comments and suggestions. **Conflicts of Interest:** The authors declare no conflicts of interest. ## References

* (1) [PERSON]; [PERSON]; [PERSON]; [PERSON] Eye Tracking for Everyone. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 26 June-1 July 2016; pp. 2176-2184.