# A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion
## 3 Robust Spatial-temporal Context Hierarchical Response Fusion
### Model Update  
Many tracking methods now update their models frame-by-frame or at a fixed interval. However, there exists some disadvantage of these methods. When the target objects go through complicated  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Precision** & **Crossing** & **Crowds** & **Couple** & **Surfer** & **Girl** & **Motor Rolling** & **David3** \\ \hline Fixed Threshold & 0.983 & 0.896 & 0.929 & 0.753 & 1 & 0.878 & 1 \\ \hline Dynamic Threshold & 1 & 0.991 & 0.929 & 0.843 & 1 & 0.957 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of fixed threshold and dynamic threshold.  
Figure 3: Partial tracking result in _Jogging-1_ sequence.  
Figure 2: Peak-to-side lobe ratio (\(PSR\)) distribution.  
scenario changes such as occlusion, scale changes, and deformation, the tracking models may have absorbed some wrong information. The wrong information will be delivered to subsequent frames and they will decrease the performance of tracking after accumulating for a long time. The end result is tracking drift.  
To solve the issues of model update, we propose a novel update method which is equivalent to active mode. In this paper, the response map peak is taken as a dynamical threshold. Since the corresponding response map peak score is different at each frame, our approach is to compare the size of \(PSR\) score and corresponding response map peak score. Only when the \(PSR\) score is greater than its maximum response peak score \(R_{max}\), will this show that the tracking result has a good performance in the current frame. In this respect, the translation discriminative filter model (see Equation (2)) and the scale discriminative filter model (see Equation (8)) will be updated online based on a learning rate parameter \(\eta\) (see Equations (3) and (9)). Or else, the proposed approach chooses not to update the tracking model in the current frame. This can effectively prevent the error information from being passed to the next frame that cause tracking drift.  
Figure 2 illustrates the distribution of the \(PSR\) scores. We can see from Figure 3 that the target objects go through complicated scenario challenges such as occlusion and background clutter from frames #61 to #79 (point A to point C), and the \(PSR\) score obviously decrease (that is, the \(PSR\) score decrease from 8.445 to 3.427). It is not suitable to update the model when the \(PSR\) score is lower than its corresponding peak score. When the target object left the occluded area at #83 (point D), the \(PSR\) score obviously increased (that is, the \(PSR\) score increase from 3.427 to 8.694). The greater the peak scores are, the more robust the tracking performance is. Under this circumstance, the update condition is met where the \(PSR\) score is greater than its corresponding peak score. The model update should be considered, and the tracking result is considered to be highly reliable in the current frame. In the case of point E (Figure 3), \(PSR\) score and its corresponding peak score has similar values. At this time, it is also not suitable to update the model.