## 4 High-Confidence Judgement Mechanism  
The model update strategy significantly impacts the precision and robustness of the tracking algorithm because the appearance of the target object varies in the tracking scenario. However, the model that is used to detect the position in the current frame is unchanged; hence, the object information that has changed cannot be obtained. Thus, a model update strategy is necessary. However, if the update frequency is too high, the problems of occlusion and motion blur cannot be solved effectively, and if the frequency is too low, the model cannot timely learn the new feature from  
Figure 2: The Visualization of Scale Space Filter Samples. the ambient environment. To overcome these challenges, a high-confidence judgement mechanism is proposed. In Figure 3, the left column contains two frames of sequence _basketball_ from OTB-15, the red bounding boxes represent the tracking results of our algorithm with the high-confidence judgement mechanism, and the blue bounding boxes represents the tracking results that are obtained when the approached judgement mechanism is not utilized and the method of updating the model is adopted in each frame. The middle column describes the cenario of severe occlusion of the target, under which the model cannot be updated. The right column presents the map response to updating the model in the same scenario. Most current trackers renovate tracking models at each frame without considering the detection precision. This might leads to deterministic failure if inaccurate detection, severe occlusion or complete objectly absence occurs. In this section, the response from the tracking results is utilized to judge whether it is necessary to renovate the tracking model or not. The peak fraction and the volatility level of the response cartographic represent the confidence level of the tracking outputs. The desired response cartographic should only have readily observable peaks and should be smooth in all other regions if the tracking outputs are specially matched to the accurate target location and scale. The more readily observable the correlation peaks are, the higher the location precision is. Otherwise, as shown in the first row of Figure 3, the response map will fluctuate violently in which the representation differs entirely from general response maps. If the tracking model is continuously updated when a deterministic failure occurs, it should be erroneous, as shown Figure 3 in the second row. Therefore, a high-confidence judgement mechanism is proposed for avoiding this scenario, which considers two standards: The first standard is the maximum response fraction \(f_{max}\) of the response cartographic \(f(x,p;\theta)\) :  
\[f_{\max}=\max f(x,p;\theta) \tag{21}\]  
The second standard, namely, the average fluctuate-except-peak energy (AFEPE) is a novel standard that is used to express the volatility level of response cartographic and the confidence degree of the detected targets which is defined as follows:  
\[AFEPE=\frac{\left|f_{\max}-f_{\min}\right|^{2}}{avg\left(\sum_{w,h}\left(f_{ w,h}-f_{\min}\right)^{2}\right)} \tag{22}\]  
Here \(f_{max}\) represents the maximum, \(f_{min}\) represents the minimum, and \(f_{w,h}\) represents the \(w\)-\(th\) row and \(h\)-\(th\) column elements of \(f(x,p;\theta)\). Figure 3: The effect of high-confidence judge mechanism. In the ideal scenario, in which the complete target appears in the detection scope, AFEPE should exceed the cartographic response. It should have only a single readily observable peak and should be smooth in all other regions. In contrast, AFEPE will dramatically decrease when the target is disappearing or occluded. While the two standards, namely, \(f_{max}\) and AFEPE, of the current frame are larger than the historical mean values that are used separately in various proportions, the tracking results in this frame are high-confidence result. Then, the tracking model will be updated online via (14), (20), and (21). Figure 3 shows the main advantage of the proposed method.