Abstract
In visual tracking task, accuracy and robustness are critical issues for achieveing remarkable performance. In this paper, we propose a novel dual path network with discriminative meta-filters and hierachical representations to solve these issues. We first design geometrically sensitivity pathway (GESP) and geographical sensitivity pathway (GASP) as two subtasks for target classification and scale estimation. GASP mainly includes powerful discriminative meta-filters to find coarse location of target and GESP can refine region of interests online while adapt the appearance model to the target swiftly. Then, a dual path network is developed in a online and offline framework. Specifically, meta-filters are trained offline in order to gain meta-knowledge of similar tracking scenes. Finally, we present three suggestions on deigning modern tracker. Extensive experiments on VOT2018 datasets verify the superior performance of proposed method compared with other state-of-the-arts, achieving expected average overlap (EAO) of 0.467.
This work is supported in part by National Major Project of China for New Generation of AI (No. 2018AAA0100400), in part by the Natural Science Foundation of China under Grant nos. 61773117, 61876088, the Primary Research & Development Plan of Jiangsu Province - Industry Prospects and Common Key Technologies under Grant No. BE2017157.
F. Xie—He is currently working toward the Master degree in the School of Automation, Southeast University.
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Xie, F., Wang, N., Yao, Y., Yang, W., Zhang, K., Liu, B. (2020). Hierarchical Representations with Discriminative Meta-filters in Dual Path Network for Tracking. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12306. Springer, Cham. https://doi.org/10.1007/978-3-030-60639-8_26
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