Visual Tracking with Reliable Memories
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 3491-3496.
Abstract
In this paper, we propose a novel visual tracking framework that intelligently discovers reliable patterns from a wide range of video to resist drift error for long-term tracking tasks. First, we design a Discrete Fourier Transform (DFT) based tracker which is able to exploit a large number of tracked samples while still ensures real-time performance. Second, we propose a clustering method with temporal constraints to explore and memorize consistent patterns from previous frames, named as reliable memories. By virtue of this method, our tracker can utilize uncontaminated information to alleviate drifting issues. Experimental results show that our tracker performs favorably against other state-of-the-art methods on benchmark datasets. Furthermore, it is significantly competent in handling drifts and able to robustly track challenging long videos over 4,000 frames, while most of others lose track at early frames.
@inproceedings{ijcai2016p493,
title = {Visual Tracking with Reliable Memories},
author = {Wang, Shu and Zhang, Shaoting and Liu, Wei and Metaxas, Dimitris N.},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {3491--3496},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/493},
}
TY - CPAPER TI - Visual Tracking with Reliable Memories AU - Wang, Shu AU - Zhang, Shaoting AU - Liu, Wei AU - Metaxas, Dimitris N. T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 3491 EP - 3496 UR - https://ijcai.org/proceedings/2016/493 ER -Copied
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