Abstract
Visual tracking has made great progress in either efficiency or accuracy, but still remain imperfections in accurately tracking on the premise of real time. In this paper, we propose a parallel network to integrate two trackers for real-time and high accuracy tracking. In our tracking framework, both trackers are based on correlation filters running in parallel, with one using hand-crafted features (tracker A) for efficiency and another using deep convolutional features (tracker B) for accuracy. And the tracking results are under supervision by a novel criterion. Furthermore, the sample models trained for correlation filter are optimized by controlling sampling frequency. For evaluation, our tracker is experimented on the datasets OTB2013 and OTB2015, demonstrating a higher accuracy than the state-of-the-art trackers on the premise of real time, especially in the situation of object deformation and occlusion.
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Acknowledgments
This work is supported by National Key Research and Development Plan under Grant No. 2016YFC0801005. This work is supported by the National Natural Science Foundation of China under Grant No. 61503388.
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Zhou, J., Wang, R., Ding, J. (2018). Deep Convolutional Features for Correlation Filter Based Tracking with Parallel Network. In: Wang, Y., Jiang, Z., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2018. Communications in Computer and Information Science, vol 875. Springer, Singapore. https://doi.org/10.1007/978-981-13-1702-6_46
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DOI: https://doi.org/10.1007/978-981-13-1702-6_46
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