# Minimum Barrier Distance-Based Object Descriptor for Visual Tracking
## 4 Experimental Results
### Quantitative Evaluation  
#### 4.3.1 Evaluation on OTB-100  
OTB-100 includes 100 video sequences with fully-annotated ground-truth and attributes. It groups 100 sequences into 11 different attributes, including illumination variation (IV), out-of-plane rotation (OPR), scale variation (SV), occlusion (OCC), deformation (DEF), motion blur (MB), fast motion (FM), in-plane rotation (IPR), out-of-view (OV), background clutters (BC) and low resolution (LR). In addition to the 29 trackers given by OTB-100, we also compared the proposed algorithm with some recent proposed tracking methods, and Table 1 shows the results of a comparison between our tracker and nine recent trackers, including SOWP [10], ACFN [18], MEEM [19], MUSTer [20], LCT (Long-term correlation tracking) [50], DSST (Discriminative Scale Space Tracker) [48], KCF (Kernelized Correlation Filters) [5], Struck [4] and TLD [21], among which ACFN [18] is a deep learning method. We used the PR/SR scores proposed in OTB-100, and the results of the trackers were obtained by running the published codes. We found that our tracker performed not well especially for the low resolution (LR) sequences, as shown in Table 1, because our algorithm took boundary patches as seeds in the bounding  
Figure 4: The visualization of the results from our tracker and SOWP [10] on several challenging sequences from OTB-100. The red bounding box is ours, and the green is SOWP. box, and the low resolution or heavy background clutter would influence the initialization of seeds, which led to unsatisfactory results in these cases. However, we observed that our tracker performed favorably against the other state-of-the-art trackers, and it achieved the best overall performance and performed well in most of the attributes. In order to evaluate the effectiveness of the proposed MBD-based object descriptor, we compared our algorithm with recent trackers and replaced the MBD with different distance metrics in Table 2. The results in Table 2 indicate that our algorithm performed well compared to several state-of-the-art algorithms including MemTrack (Dynamic Memory Networks) [51], scaleDLSSVM (Dual Linear Structured SVM) [29], DCFNet (Discriminant Correlation Filters Network) [52], CNN-SVM (Convolutional Neural Network) [8], SiamFC-3s (Fully-Convolutional Siamese Networks) [53] and SINT++ (Siamese Network) [2]. Of these trackers, MemTrack [51] and SINT++ [2] are neural network-based methods; scaleDLSSVM [29] and CNN-SVM [8] are tracking-by-detection-based methods; DCFNet [52] and SiamFC-3s [53] are correlation filter-based methods. In addition to the latest tracker MemTrack, which obtained comparable results to ours, our method was better than other state-of-the-art algorithms. OURS-Eu, OURS-L1 and OURS-KL are the results of replacing our MBD with Euclidean distance, L1 distance and KL divergence, respectively. To verify the effectiveness of MBD-tuned weights, we also created a version of our algorithm with random weights subjected to Gaussian distribution, named OURS-Ga. Our method also gets better results than OURS-Eu, OURS-L1, OURS-KL and OURS-Ga, which verifies the effectiveness of MBD-tuned weighting. We rank the top 16 trackers by running the one-pass evaluation (OPE) on the OTB-100 as shown in Figure 5. MemTrack [51] is the latest method based on a deep network and obtained comparable results to ours. Our tracker (0.835/0.995) achieved performance gains of 5.4% in precision and 6.5% in success rates over MEEM [19] (0.781/0.530), even if MEEM may have recovered from the corrupted classifier by utilizing a multi-expert tracking framework. Our tracker outperformed the tracker SOWP [10] (0.803/0.560) with 3.2%/3.5% in precision and success rates, respectively. That means that our method had a more robust descriptor than SOWP, which also used patch descriptors. We present the precision plots and success plots with eight different attributes in Figure 6 such as DEF, OPR, SV, OCC, OV, MB, FM and BC.