# A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion
## 3 Robust Spatial-temporal Context Hierarchical Response Fusion
### Algorithm Flowchart
In the Ironman, Soccer and Box sequences, the target mainly goes through complicated scenarios challenges, for instance scale variation, motion blur, and background clutters. Most of the trackers such as C-COT_HOG, SRDCF, STAPLE_CA, SAMF_AT and CNN-SVM trackers lose the target and fail to recover. Benefit from the re-detection activation strategy and the self-adaptive model updates strategy, the proposed tracker can track the target successfully, which further validate the effectiveness of the proposed methods. Figure 5: Attribute-based evaluation of the distance precision and overlap success plots compare the proposed algorithm with 13 state-of-the-art trackers over four video attributes of (**a**) background clutter, (**b**) occlusion, (**c**) in-plane rotation, (**d**) illumination variation, and (**e**) scale variation. Figure 6: _Cont._

## 5 Conclusions

In this paper, we use hierarchical features of CNNs for visual tracking. We make full use of the characteristics of each convolution layer to locate the object target. The semantic information of deep convolution has great significance for the change of the appearance of the target. The spatial details of shallow convolutional layer enable to locate targets position accurately. In addition, we train a spatial-temporal context filter on convolutional layers and predict the target position by fusing the response value of the filters on the three convolutional layers. Moreover, we proposed a re-detection method and model update method by comparing the size of the _PSR_ and its corresponding response value to determine whether to re-detect or update the model. The experimental results demonstrate that the proposed algorithm with deep and HOG features outperforms most state-of-the-art algorithms based on DCFs. Writing--original draft, W.Z. ; Writing--review & editing, Y.L., Z.C., Y.D., D.Z. and P.L. Funding: This work was supported by the Quanzhou scientific and technological planning projects, Fujian, China (No.2018C113R), and the grants from National Natural Science Foundation of China (Grant No.61605048), in part by Natural Science Foundation of Fujian Province, China under Grant 2016J01300, in part by Fujian Provincial Big Data Research Institute of Intelligent Manufacturing, Quanzhou, 362021, China, in part by Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University (No.ZQN-PY518), and in part by the Subsidized Project for Postgraduates' Innovative Fund in Scientific Research of Huaqiao University under Grant 17014084014. Thanks to my family for their support. Thanks to my teachers and classmates for giving me guidance on my studies. Thanks to my school Huaqiao University. The authors declare no conflict of interest. ## References

* [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Visual Tracking: An Experimental Survey. _IEEE Trans. Pattern Anal. Mach. Intell._**2014**, _36_, 1442-1468. [PubMed]
* [PERSON] et al. (2006) [PERSON]; [PERSON]; [PERSON] Object tracking: A survey. _ACM Comput. Surv._**2006**, _38_, 13. [CrossRef]
* [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON] Online Object Tracking: A Benchmark. In Proceedings of the IEEE Computer Vision and Pattern Recognition, Portland, OR, USA, 23-28 June 2013; pp. 2411-2418. * [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON] Object Tracking Benchmark. _IEEE Trans. Pattern Anal. Mach. Intell._**2015**, _37_, 1834-1848. [CrossRef] [PubMed]

Figure 6: A qualitative comparison of our approach with six trackers, C-COT_HOG [23], SRDCF [21], STAPLE_CA [13], LMCF [22], SAMF_CA [13], CNN-SVM [44]. Tracking results are shown on five video sequences from the OTB dataset (from top to down are _Bird2_, _Dragon Baby_, _Ironman_, _Soccer_, _Box_). Our approach performs favorably compared to the existing tracker in these challenging situations. * [PERSON] et al. (2010) [PERSON]; [PERSON]; [PERSON]; [PERSON] Visual object tracking using adaptive correlation filters.