# Robust Long-Term Visual Object Tracking via Low-Rank Sparse Learning for Re-Detection
## 4 Experiment
### Results and Analysis  
#### 4.2.1 OTB-2013 Benchmark  
(1) Comparison with the baseline  
In this work, we estimate the OTB-2013 dataset, which includes 51 challenging video sequences. Here, we choose Staple [15] as the baseline, which extracts HOG and color histogram as the appearance feature model and enables greater accuracy. To address the issues of the target detection again after tracking failure and the interference from complex scenes, we improve this algorithm and obtain good performance for long-term tracking. Our primary contribution is introducing the motion features into correlation tracking as a complement to appearance features. The secondary contribution is introducing a re-detection module based on sparse and low-rank representation for long-term tracking. The third contribution is proposing a reliability estimation method, which takes into account the reliability of the result spatially and temporally. In Figure 5, we analyze these contributions in detail. In Figure 5, on the whole, our proposed method provides an obvious performance improvement over the baseline tracker (Staple) by a success rate gain of \(11.8\%\) and a precision gain of \(12.4\%\). More specifically, after introducing the motion features, our tracker provides a performance improvement by a success rate gain of \(2.2\%\) and a precision gain of \(2.7\%\). On this basis, it can boost the success rate of \(8.8\%\) and the precision of \(8.8\%\) in by introducing a re-detection module based on sparse and low-rank representation for long-term tracking. Besides, our tracker can further improve the performance by a success rate of \(0.8\%\) and a precision of \(0.9\%\). In conclusion, all of our contributions can effectively improve the tracker's performance, but the second one has the most significant improvement. (2) Integration into Different DCF Trackers  
Here, we combine multiple classical correlation filter based tracking algorithms with our proposed improvement method. In Figure 6, we compare these different DCF trackers with their improved algorithms. Figure 5: Precision plots (**left**) and Success plots (**right**) of OPE on OTB-2013 dataset. The values in the legend indicate the quantitative comparisons of distance precision at the threshold of 20 pixels and overlap success rate at the conventional thresholds of \(0.5\) (\(IOU>0.5\)). After introducing our proposed improvement method, the basic DCF based trackers (KCF [12], DSST [13], Staple [15]) all gain significant improvements. For example, the improved KCF algorithm has a success rate gain of 5.0% and a precision gain of 6.4%. The improved DSST algorithm boosts the success rate by 8.4% and the precision by 4.1%. Besides, the Staple tracker still obtains an obvious improvement by a success rate gain of 11.8% and a precision gain of 12.4%. The results show that it is feasible to improve the performance of a DCF-based tracker by introducing our proposed improvement methods. (3) Comparison with the state-of-the-art trackers  
We have done plenty of comparative experiments with these representative state-of-the-art trackers. These trackers include the DCF-based trackers (i.e., SRDCF [17], ECOhc [18], Staple [15], DSST [13], KCF [12], CSR-DCF [39], SAMF [14], SAMF_CA [40]) and the trackers for long-term tracking (i.e., TLD [42], LCT [33]). In Figure 7, our method outperforms these state-of-the-art methods in the success rate of 83.2% and the precision of 89.2%. (4) Qualitative Analysis  
For a further detailed analysis of these trackers, we provide the tracking results parameterized by different attributes in Figure 8, including background clutter (BC), deformation (DEF), fast motion (FM), in-plane rotation (IPR), illumination variation (IV), low resolution (LR), motion blur (MB), deformation (DEF), occlusion (OCC), out of view (OV), out-of-plane rotation (OPR) and scale variation (SV). Table 1 shows that our proposed tracker is superior to the baseline tracker in different attributes on the success rate and precision intuitively.