# Learning Spatial-Temporal Background-Aware Based Tracking
## 4 Experiments
### The Overall Tracking Results on OTB Dataset  
We compare the proposed method with 10 object tracking methods, including ECO-HC [20], GradNet [39], MCPF [11], DeepSRDCF [40], DeepLMCF [41], CREST [19], UCT [42], ARCF [25], SRDCF [28] and STAPLE_CA [6] on a well-known tracking benchmark datasets [43; 44], which contains almost 50 and 100 video annotations video sequences separately with 11 different attributes. We evaluate the performance of 10 trackers by using two metrics provided in [43] on OTB-50 [44] and OTB-100 dataset, the results of tracking are displayed by the report of overlapping success rate and range accuracy. The distance precision (DP) shows that the ratio of frames whose center location error is within a certain threshold. The overlap success plot, which shows the number of thresholds given is less than the overlap number of bounding boxes, and generally, the given threshold of OS is set to 0.5. We report by using some data provided by [43] through the evaluation protocol, the data is applied to the tracking performance of the protocol, and the overlapping success graph and precision graph on these data sets are used. The results are shown in Figures 2 and 3 respectively.  
We compare 10 kinds of newly discovered OS methods with DP methods, and the proposed tracker performs well with DP of 91.1% and OS of 64.3%. It can be easily seen from Figure 1 that our tracker performs favorably against the other existing methods; in particular, it significantly outperforms the MCPC, GradNet and DeepLMCF trackers that utilize visual deep features.  
It can be easily seen from Figure 3 the proposed tracker performs well with DP of 89.9% and OS of 64.4%, where the average DP of 89.9 outperformed the recent state-of-the-art trackers such as MCPF (88.3), GradNet (87.6), DeepLMCF (87.4), UCT (85.9), ECO-HC (84.9), DeepSRDCF (84.7), CREST (80.1), ARCF (77.8), STAPLE_CA (77.1), SRDCF (76.2), and the average OS of 66.4 outperformed ECO-HC (63.5), MCPF (63.1), GradNet (62.3), DeepSRDCF (62.9), DeepLMCF (62), CREST (61.9), UCT (61.2), ARCF (59.1), SRDCF (57.6), STAPLE_CA (54.8). These tracking results demonstrate the validity of introducing the spatial-temporal module to DCF based framework.  
Table 1 shows the mean FPS results of the proposed tracker and some mainstream trackers. It can be seen from the table that the proposed tracker combines traditional hand-crafted and deep network features, which significantly increases the amount of calculation and makes the tracker's speed worse than STRCF and BACF, which only use hand-crafted trackers but still outperform some trackers such as SRDCF and MCPF.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **ARCF** & **STRCF** & **BACF** & **SRDCF** & **STAPLE\_CA** & **MCPF** & **Ours** \\ \hline Mean FPS & 15.3 & 24.2 & 26.7 & 3.8 & 35.3 & 1.8 & 5 \\ \hline \end{tabular}
\end{table}
Table 1: The mean FPS results of the proposed trackers with several mainstream tracking methods on OTB-2015.  
Figure 3: The overall tracking performances of precision and overlap success plots on OTB-100 dataset using OPE evaluation.  
Figure 2: The overall tracking performances of precision and overlap success plots on OTB-50 dataset using OPE evaluation.  
Figure 4 shows the attribute-based evaluation results of six video attributes on OTB50. The results demonstrate proposed tracker outperforms existing competing trackers, this empirically shows how combining spatial-temporal regularization with background-aware module improves the reliability of such trackers against complex boundary effect and appearance variations. Trackers such as MCPF, ECO-HC and DeepLMCF have shown to be less robust to background clutter, out of view, deformation, illumination and variation, respectively.