# Robust Visual Tracking via Patch Descriptor and Structural Local Sparse Representation
## 4 Object Tracking
That was attributed to the added outlier detect module, which alleviated the problem where outlier samples are inadvertently included by the straightforward template update method in the ASLA. ### Typical Results Analysis

We provided more detailed analysis on 12 representative sequences selected from the OTB-2013 benchmark dataset. Tables 1 and 2 report the comparison results of our tracker and 9 other trackers in terms of the average center location error and average overlap rate. More accurate trackers had lower center location errors and higher overlap rates. Through the results of the tables, we can see that our tracking method obtained smaller center location errors and higher overlap rates on these challenging sequences. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & **VTD** & **Frag** & **TLD** & **LIAPG** & **STC** & **CSK** & **DLT** & **CT** & **ASLA** & **Ours** \\ \hline Singer1 & 4.19 & 88.87 & 8.00 & 53.35 & 5.76 & 14.01 & **3.04** & 15.53 & 3.29 & **2.98** \\ David & 11.59 & 82.07 & 5.12 & 13.95 & 12.16 & 17.69 & 66.20 & 10.49 & **5.07** & **4.36** \\ Freeman3 & 23.96 & 40.47 & 29.33 & 33.13 & 39.44 & 53.90 & 4.00 & 65.32 & **3.17** & **2.06** \\ CarScale & 38.45 & **19.74** & 22.60 & 79.77 & 89.35 & 83.01 & 22.65 & 25.95 & 24.64 & **14.11** \\ [PERSON] & **10.30** & 82.69 & 18.05 & 23.46 & 25.60 & 13.39 & **8.81** & 26.53 & 15.26 & 11.82 \\ Crossing & 26.13 & 38.59 & 24.34 & 63.43 & 34.07 & 8.95 & **1.65** & 3.56 & 1.85 & **1.54** \\ Walking2 & 46.24 & 57.53 & 44.56 & 5.06 & 13.83 & 17.93 & **2.18** & 58.53 & 37.42 & **1.95** \\ Freeman4 & 61.68 & 72.27 & 39.18 & **22.12** & 45.61 & 78.87 & 48.09 & 132.59 & 70.24 & **4.57** \\ David3 & 66.72 & 13.55 & 208.00 & 90.00 & **6.34** & 56.10 & 55.87 & 88.66 & 87.76 & **6.39** \\ FaceOcc1 & 20.20 & **10.97** & 27.37 & 17.33 & 250.40 & **11.93** & 22.72 & 25.82 & 78.06 & 13.58 \\ Skating1 & **9.34** & 149.35 & 145.45 & 158.70 & 66.41 & **7.79** & 52.38 & 150.44 & 59.86 & 15.47 \\ Football & 13.64 & **5.36** & 14.26 & 15.11 & 16.13 & 16.19 & 191.4 & 11.91 & 15.00 & **4.13** \\ Average & **27.70** & 55.12 & 48.86 & 47.95 & 50.43 & 31.65 & 39.92 & 51.23 & 33.47 & **6.91** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average center error (in pixels). The best two results are shown in bold red and blue fonts. Figure 7: Evaluation of template update strategy on the OTB-2013 benchmark dataset. Figures 8-11 show some screenshots of the tracking results, marked by different colorful bounding boxes. For these sequences, several principal factors that have effects on the appearance of an object were considered. Some other factors were also included in the discussion. **Occlusion:** The sequences FaceOcc1, Football and Walking2 were chosen to demonstrate the effect of partial occlusion. In the FaceOcc1 sequence, all trackers except the STC and ASLA could track the object properly, and the ASLA lost the object when the face occlusion happened, as shown in Figure 8a. The Football sequence (Figure 8b) contained occlusion and background clutters. When the tracked object came into the dense group of players (e.g., #120 and #192), the DLT lost the object. For the surveillance video Walking2 (Figure 8c), the walking woman was occluded by a man over a long time. The Frag, VTD, TLD and CT lost the object when occlusion occurred (e.g., #269 and #385). The STC and CSK did not scale well when the scale of object changed. Only the L1 APG, DLT and our method could accurately track the object till the end. **Deformation:** Figure 9 presents some sampled results in three sequences where objects underwent deformations. In the Crossing sequence (Figure 9a), the walking person moved from a shadow area to a bright one. Nonrigid deformation and drastic illumination variation were the main challenges. The VTD, [PERSON], TLD, L1 APG and STC lost the object in the tracking process (e.g., #55 and #95). For the David3 sequence (Figure 9b), occlusion was introduced by tree (e.g., #88) and object appearance changed drastically when the man turned around (e.g., #161).