# A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion
## 3 Robust Spatial-temporal Context Hierarchical Response Fusion
### Algorithm Flowchart  
The proposed tracking algorithm is showed in Algorithm 1. ``` Input: Initial target location \(P_{0}\) (\(x_{0}\), \(y_{0}\)), initial scale \(S_{0}\) (\(w_{0}\), \(v_{0}\)), initial \(PSR\) score \(PSR_{0}\), hierarchical correlation filters \(\{W_{t}^{l}\ |l=3,4,5\}\). Output: Estimated object location \(P_{t}\) (\(x_{t}\), \(y_{t}\)), estimated scale \(S_{t}\) (\(w_{t}\), \(v_{t}\)). 1. Repeat:
2. Crop out the image samples centered at \(P\) (\(x_{t}\), \(y_{t}\)) and extract convolutional features and HOG features;
3. For each layer \(l\) computes the response map \(R_{context}\) via Equation (2);
4. Estimate the location of the target by computing the maximum response map after fusion \(R_{t}\) via Equation (6);
5. Construct a target scale pyramid around \(P\) (\(x_{t}\), \(y_{t}\)) and estimate the optimal scale of the target as in Equation (10);
6. Calculate the \(PSR\) score of the response map peak;
7. **If \(PSR_{t}<R_{max}\), then**
8. Activate re-detecting component D and find the possible candidate states \(C\);
9. For each state \(C_{d}\) in \(C\), do computing response score \(R_{context}\) via Equation (2);
10. **End if**
11. **If \(PSR_{t}>R_{max}\), then**
12. Update the tracking model via Equations (3a) and (3b);
13. Update the scale estimation model via Equations (9a) and (9b);
14. Update detector D;
15. **End if**
16. **Until** end of video sequence. ## 4 Experimental Results

### Implementation Details

The proposed tracker is implemented in MATLAB2014a on a PC with an i7 3.2 GHz CPU with 16 GB memory. The feature extractor we employ is pre-trained VGGNet-16 trained on the ImageNet dataset. We also use HOG features in 4 \(\times\) 4 window (a cell size of 4 \(\times\) 4). We set the size of the search window to 2.2 times the size of the target object. The spatial bandwidth is set to 1/10. The learning rate parameter \(\eta\) in Equations (3) and (9) is set to 0.025. The number of scales (\(S\)) is set to 33, with a scale-step of 1.02. The \(PSR_{0}\) (the \(PSR\) initial value) is set to 1.8. We employ the same parameters for each video sequence. The position of the accuracy degradation is different for different video sequences (Table 2). ### The Overall Tracking Performance on OTB Benchmark Datasets

We evaluate the proposed tracker, and it compares with the state-of-the-art 13 trackers including C-COT_HOG [23], SRDCF [21], CFNet_conv3 [41], SiamFC_3s [42], STAPLE_CA [13], Staple [17], LMCF [22], SAMF_AT [43], SAMF_CA [13], LCT [12], DSST [16], KCF [6], and some CNN based tracker, including CNN-SVM [44] on a large standard benchmark dataset [3], [4] that contains 100 videos. We use three metrics provided in [3] and [4] to evaluate 11 trackers on OTB-100, and display the tracking results by two indicators (Distance Precision (DP) and Overlap success Precision (OP)). We evaluate the tracking performance based on one-pass evaluation (OPE) protocol provided by [3] and [4]. As shown in Figure 4, we use DP plot and OP plot for presentation and employ area under the curve (AUC) success plots to rank these trackers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence** & **Scale-Step** & **Precision** \\ \hline \multirow{5}{*}{_Jogging-1_} & 1.02 & 0.974 \\ \cline{2-3}  & 1.03 & 0.974 \\ \cline{2-3}  & 1.04 & 0.974 \\ \cline{2-3}  & 1.05 & 0.974 \\ \cline{2-3}  & 1.2 & 0.915 \\ \cline{2-3}  & 1.3 & 0.863 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Different scale-step values and their corresponding precision. Figure 4: The overall tracking performances of precision (**a**) and success plots (**b**) comparing the proposed tracker with state-of-the-art trackers on OTB-100 using OPE evaluation. The proposed tracker performs well against these algorithms. Figure 4 illustrates DP plot and OP plot of 12 trackers on OTB-100 benchmark datasets. As can be seen in the figure, the proposed tracker performs favorably against state-of-the-art trackers in distance precision (DP) and overlap success precision (OP).