# Learning Spatial-Temporal Background-Aware Based Tracking
## 4 Experiments
### The Qualitative Evaluation  
Figure 5 shows a qualitative comparison between the proposed tracker and the 10 most advanced trackers (MCPF, GradNet, SRDCF, ECO-HC, UCT, ARCF, DeepSRDCF, STAPLE_CA, DeepLMCF and CREST) on five representative challenging sequences (Motor-Rolling, DragonBaby, Skiing, Bolt2 and Matrix) from OTB-2015. From the figure, we can see that the proposed tracker deals well with motion blur, fast motion, deformation, scale variation, out-of-view, out-plane rotation, occlusion and background clutter scenarios challenges.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tracker Name** & **DP** & **OS** \\ \hline ECO & 0.741 & 0.605 \\ ECO-HC & 0.726 & 0.551 \\ MCPF & 0.774 & 0.545 \\ BACF & 0.648 & 0.519 \\ TADT & 0.756 & 0.562 \\ PTAV & 0.742 & 0.546 \\ DeepSRDCF & 0.738 & 0.536 \\ STAPLE & 0.668 & 0.497 \\ SRDCF & 0.675 & 0.485 \\ STRCF & 0.723 & 0.553 \\ Ours & 0.780 & 0.566 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The overall tracking results on the TC-128 datasets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Tracker Name** & **OS** \\ \hline ECO-HC & 0.507 \\ DSST & 0.448 \\ SRDCF & 0.465 \\ BACF & 0.519 \\ STAPLE\_CA & 0.562 \\ STAPLE & 0.546 \\ KCF & 0.406 \\ SAMF & 0.485 \\ ARCF & 0.600 \\ Ours & 0.572 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The overall tracking results on the UAV123 datasets.  
In the Matrix and MotorRolling sequences, the object mainly experiences significant appearance changes such as scale variation and background clutter challenges. Most of the trackers such as GradNet, ARCF, CREST, UCT, ECO-HC, DeepSRDCF, DeepLMCF and STAPLE_CA lose the target and fail to recover from tracking drift, therefore, compared with the traditional hand-crafted feature or CNN feature, it is more effective to fuse the hand-crafted feature and multiple powerful hierarchical convolution features. However, due to the proposed method, the proposed tracker can deal effectively with challenges of motion blur and occlusion.  
As for the skiing, DragonBaby and Bolt2 sequences, the object undergoes a certain degree of appearance change, such as fast motion, deformation and scale variance. The proposed tracker locates the target accurately from #28 to #34 in Skiing sequence, and performs more robust than ECO-HC, ARCF, SRDCF and DeepSRDCF trackers. GradNet, MCPF, ECO-HC, UCT, ARCF, CREST, DeepSRDCF, DeepLMCF and STAPLE_CA trackers fail to track the target object successfully from #46 to #81 in the DragonBaby sequence when fast motion and large-scale scale change occurs. GradNet, ARCF, ECO-HC, UCT, CREST and SRDCF in the sequence with other features, such as bolt2 sequence deformation and background clutter, the performance of the tracker is not so good. Even so, through the experiment, it is found that the tracker can still locate the related targets well, which further verifies the reliability of the proposed method.  
Figure 5: Qualitative evaluation of the proposed tracker with comparison to 10 trackers on 5 challenge sequences (from left to right and top to down are Matrix, MotorRolling, Skiing, DragonBaby and Bolt2).