Deep Meta Learning for Real-Time Target-Aware Visual Tracking

Janghoon Choi, Junseok Kwon, Kyoung Mu Lee; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 911-920


In this paper, we propose a novel on-line visual tracking framework based on the Siamese matching network and meta-learner network, which run at real-time speeds. Conventional deep convolutional feature-based discriminative visual tracking algorithms require continuous re-training of classifiers or correlation filters, which involve solving complex optimization tasks to adapt to the new appearance of a target object. To alleviate this complex process, our proposed algorithm incorporates and utilizes a meta-learner network to provide the matching network with new appearance information of the target objects by adding target-aware feature space. The parameters for the target-specific feature space are provided instantly from a single forward-pass of the meta-learner network. By eliminating the necessity of continuously solving complex optimization tasks in the course of tracking, experimental results demonstrate that our algorithm performs at a real-time speed while maintaining competitive performance among other state-of-the-art tracking algorithms.

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[pdf] [supp]
author = {Choi, Janghoon and Kwon, Junseok and Lee, Kyoung Mu},
title = {Deep Meta Learning for Real-Time Target-Aware Visual Tracking},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}