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Query-Based Object Visual Tracking with Parallel Sequence Generation

delete2024-07-24
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OA
AI
C
Chang Liu
张彬 封面图
张彬 (Bin Zhang)
C
Chunjuan Bo *
D
Dong Wang
DOI:10.3390/s24154802delete
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摘要

摘要

En 中文
Query decoders have been shown to achieve good performance in object detection. However, they suffer from insufficient object tracking performance. Sequence-to-sequence learning in this context has recently been explored, with the idea of describing a target as a sequence of discrete tokens. In this study, we experimentally determine that, with appropriate representation, a parallel approach for predicting a target coordinate sequence with a query decoder can achieve good performance and speed. We propose a concise query-based tracking framework for predicting a target coordinate sequence in a parallel manner, named QPSTrack. A set of queries are designed to be responsible for different coordinates of the tracked target. All the queries jointly represent a target rather than a traditional one-to-one matching pattern between the query and target. Moreover, we adopt an adaptive decoding scheme including a one-layer adaptive decoder and learnable adaptive inputs for the decoder. This decoding scheme assists the queries in decoding the template-guided search features better. Furthermore, we explore the use of the plain ViT-Base, ViT-Large, and lightweight hierarchical LeViT architectures as the encoder backbone, providing a family of three variants in total. All the trackers are found to obtain a good trade-off between speed and performance; for instance, our tracker QPSTrack-B256 with the ViT-Base encoder achieves a 69.1% AUC on the LaSOT benchmark at 104.8 FPS.
Keyword:
visual tracking
object tracking
transformer
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期刊

Sensors 封面图
Sensors
IF:
3.5
论文数:
7.3W
被引数:
23.2W

机构

D
dalian university of technology
学者数:
5.0K
论文数: 1.4K
被引数: 2
D
Dalian Minzu University
学者数:
260
论文数: 82
被引数: 1
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