# A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion
## 3 Robust Spatial-temporal Context Hierarchical Response Fusion
### Algorithm Flowchart
DP is computed as the relative number of frames in the sequence where the center location error is smaller than a certain threshold, and the DP values at a threshold of 20 pixels are reported. OP is defined as the percentage of frames where the bounding box overlap larger than a given threshold and the initial threshold value of overlap success (OP) is generally set to 0.5. Table 3 shows the results from the proposed tracker and the state-of-the-art trackers. The proposed tracker performs better with DP of 87.6% and OP of 62.7%, where the DP and OP surpassed other trackers. The comparison of these tracking results prove the effectiveness of the proposed methods. The last column of bold Proposed is the best result. The proposed tracker mainly employs the hierarchical features of CNNs for feature representation and the tracking speed is 6 frames per second, the main time-consuming burden of the proposed tracker is the process of extracting deep convolutional features. The next work guarantees robustness while improving tracking speed. ### The Attribute-Based Tracking Evaluation

We also perform an attribute-based analysis of our approach. In the OTB dataset, all videos are annotated with 11 different attributes, namely: illumination variation, motion blur, fast motion, in-plane rotation, scale variation, background clutter, deformation, out of view, out-of-plane rotation, occlusion and low resolution. Due to the limited space, we only display 5 attributes results for representation in Figure 5.

Figure 5 illustrates that the proposed tracker compares to 13 state-of-the-art trackers, and the result use DP plot and OP plot to show four different challenges attributes. It is clearly demonstrated that proposed tracker obtains superior DP and OP in background clutter (83.9%, 59.7%), occlusion (89.1%, 63.8%), in-plane rotation (86.2%, 61.0%), illumination variation (83.7%, 60.8%), and scale variation (86.6%, 60.2%). In sequences annotated with the background clutter attribute, fast motion attribute and in-plane rotation attribute, our approach outperforms the compared trackers. Benefit from the re-detecting method and self-adaptive model update method, the proposed tracker can adaptively activate the re-detection and update tracking model in the case of tracking failure. This shows that our tracker is highly robust and achieves superior performance in different challenging scenarios. ### Qualitative Evaluation

Here we provide a qualitative comparison result of our approach with six state-of-the-art trackers from the literature (C-COT_HOG, SRDCF, STAPLE_CA, LMCF, SAMF_AT, and CNN-SVM). Figure 6 illustrates frames from five sequences with scale variation (Dragon Baby, Ironman, Soccer, Box), occlusion (Dragon Baby, Ironman, Soccer, Box, Bird2), background clutters (Ironman, Soccer, Box), motion blur (Dragon Baby, Ironman, Soccer, Box), in-plane rotation (Dragon Baby, Ironman, Soccer, Box, Bird2), out-of-plane rotation (Dragon Baby, Ironman, Soccer, Box, Bird2), illumination variation (Ironman, Soccer, Box). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **KCF** & **DSST** & **LCT** & **CNN-SVM** & **SAMF\_CA** & **SAMF\_AT** & **LMCF** \\ \hline
**DP** & 0.686 & 0.690 & 0.758 & 0.823 & 0.769 & 0.795 & 0.789 \\ \hline
**OP** & 0.471 & 0.518 & 0.525 & 0.554 & 0.555 & 0.571 & 0.579 \\ \hline  & **Staple** & **SiamFC\_3s** & **STAPLE\_CA** & **SRDCF** & **CFNet\_conv3C-COT\_HOG** & **Proposed** \\ \hline
**DP** & 0.790 & 0.781 & 0.817 & 0.793 & 0.791 & 0.808 & 0.876 \\ \hline
**OP** & 0.583 & 0.590 & 0.599 & 0.599 & 0.601 & 0.604 & 0.627 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with state-of-the-art trackers on the OTB dataset. The results are presented in terms of distance precision (DP) and overlap success precision (OP). Figure 5: _Cont_. In the Bird2 and Dragon Baby sequences, the compared trackers struggle due to occlusion, in-plane rotation and out-of-plane rotation, while our tracker robustly handles these factors.