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Learning Regression and Verification Networks for Robust Long-term Tracking.
- Source :
-
International Journal of Computer Vision . Sep2021, Vol. 129 Issue 9, p2536-2547. 12p. - Publication Year :
- 2021
-
Abstract
- This paper proposes a new visual tracking algorithm, which leverages the merits of both template matching approaches and classification models for long-term object detection and tracking. To this end, a regression network is learned offline to detect a set of target candidates through target template matching. To cope with target appearance variations in long-term scenarios, a target-aware feature fusion mechanism is also developed, giving rise to more effective template matching. Meanwhile, a verification network is trained online to better capture target appearance and identify the target from potential candidates. During online update, contaminated training samples can be filtered out through a monitoring module, alleviating model degeneration caused by error accumulation. The regression and verification networks operate in a cascaded manner, which allows tracking to be performed in a coarse-to-fine manner and enforces the discriminative power. To further address the target reappearance issues in long-term tracking, a learning-based switching scheme is proposed, which learns to switch the tracking mode between local and global search based on the tracking results. Extensive evaluations on long-term tracking in the wild have been conducted. We achieve state-of-the-art performance on the OxUvA long-term tracking dataset. Our submission based on the proposed method has also won the 1st place of the long-term tracking challenge in VOT-2018 competition. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09205691
- Volume :
- 129
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- International Journal of Computer Vision
- Publication Type :
- Academic Journal
- Accession number :
- 151648991
- Full Text :
- https://doi.org/10.1007/s11263-021-01487-3