# Robust Visual Tracking via Patch Descriptor and Structural Local Sparse Representation
## 4 Object Tracking
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0: Observation vector \(\mathbf{r}\), eigenvectors \(\mathbf{U}\), average observation \(\mathbf{\mu}\), outlier ratio \(\eta\), thresholds tr1 and tr2, template set \(\mathbf{T}\), the current frame\(f\) (\(f>\) n)
1: if mod(\(f\),5) = 0 and \(\eta\leq\) tr2 then
2: Generate a sequence of number in ascending order and normalize them into [0, 1] as the probability for template update;
3: Generate a random number between 0 and 1 which is for the selection of which template to be discarded;
4: if \(\eta<\) tr1
5: Solve Equation (8) and obtain \(\mathbf{q}\) and \(\mathbf{e}\);
6: Add \(\hat{\mathbf{r}}=\mathbf{U}\mathbf{q}\) to the end of the template set \(\mathbf{T}\);
7: else if tr1 \(\leq\eta\leq\) tr2
8: Solve Equation (10) and obtain the recovered sample \(\mathbf{r}_{\mathrm{new}}\);
9: Solve Equation (8) and obtain \(\mathbf{q}\) and \(\mathbf{e}\);
10: Add \(\hat{\mathbf{r}}_{\mathrm{new}}=\mathbf{U}\mathbf{q}\) to the end of the template set \(\mathbf{T}\);
11: end if
12: end if
13: Compute \(\mathbf{D}\) and \(\mathbf{D}\) using \(\mathbf{D}\

## 6 Experiments

### Experiment Settings

The proposed tracker was implemented in MATLAB R2017a on a PC with Intel i5-7400 CPU (3.0 GHz) and 16 GB memory. We evaluated the tracking performance on the OTB-2013 benchmark dataset [26] that contains 51 challenging sequences. The parameters of our tracker for all test sequences were fixed to demonstrate its robustness and stability. The target region division was shown in Section 3.1. The number of target templates \(n\) was set to 10. We manually labeled the location of the target in the first frame for each sequence and set K = 40, \(\lambda\) = 0.01 and \(\gamma\) = 0.01. The number of particles was 600 and the variance matrix of affine parameters was set as \(\Sigma\) = diag (4, 4, 0.01, 0.005). For the template update, ten eigenvectors were used to carry out the incremental subspace learning method every five frames. To evaluate the performance of the trackers, we adopted three widely used metrics [26]: (i) the center location error (CLE), which computes the average Euclidean distance between the center locations of the tracked targets and the ground truth positions of all the frames; (ii) distance precision, which is calculated as the percentage of tracking frames, where the estimated locations are within a given threshold distance; (iii) the success rate, which is calculated as the percentage of tracking frames where the bounding box overlap rate surpasses a given threshold. The overlap rate is defined by PASCAL VOC criteria [40], score = \(\frac{area(\text{ROI}_{\text{T}}\cap\text{ROI}_{\text{C}})}{area(\text{ROI}_ {\text{T}}\cup\text{ROI}_{\text{C}})}\), where \(\text{ROI}_{\text{T}}\) is the tracking bounding box and \(\text{ROI}_{\text{G}}\) is the ground truth bounding box. \(\cap\) and \(\cup\) stand for the intersection and union of two regions in pixels, respectively. Figure 3: An illustration of the variation of the outlier ratio \(\eta\) on the FaceOcc1 sequence. In the bottom plot, the blue curve reflects the variation of the outlier ratio on each frame, and the two horizontal red dashed lines are the predefined thresholds (set to 0.1 and 0.35 in our approach). We also mark the key frames and their indices with red points in the plot, which are correspondingly shown at the top of the plot. ### Overall Performance

We compared the proposed tracker with nine other trackers, including VTD [5], L1 APG [7], ASLA [9], CSK [18], Frag [27], TLD [35], STC [41], DLT [42], CT [43]. Among them, the ASLA, VTD and Frag are patch-based tracking methods. The CSK and STC are based on CFs. The DLT is based on deep neural networks. The CT is based on the compressive sensing theory. We used the results proposed by the OTB-2013 benchmark or available source codes to reproduce the results.