MFCFSiam: A Correlation-Filter-Guided Siamese Network with Multifeature for Visual Tracking

Figure 2

The basic workflow of our proposed tracking framework. The CF tracker based on HOG and CN features is used to guide the SiamFC tracker. The SiamFC tracker and the CF tracker produce their own tracking results. The validity evaluation filter uses the initial groundtruth in the first frame to generate a robust filter, and this filter is used to evaluate the two tracking results’ validity. Correlation computation is conducted between this filter and each of the two results, and two response maps are produced. Finally, the result that has the bigger maximum on its response map is considered to be the final result, and this result is also used to update the SiamFC tracker.