A multi-scale kernel correlation filter tracker with feature integration and robust model updater

In recent years, the correlation filter-based trackers (CFTs) have shown to provide excellent results in different competitions and benchmarks, but there is still a need to improve the robustness of CFTs. Compared with the traditional kernel correlation filter tracker, the approach we present in this paper makes some significant improvements. The strong features including HOG and Color-naming are integrated to maintain a more powerful object representation, and PCA is applied to boost the computation speed. To deal with scale variation, a multiple scale adaptive method is adapted. Model updater is modified by considering all previous frames to update the discriminative classifier coefficients. Extensive evaluations are performed on 51 challenging benchmark sequences. The experiments results show that our approach outperforms state-of-the-art tracking methods. Additionally, the proposed approach is implemented in our practical project, and the results also prove the availability of our approach.