Comparison on long-term tracking: methods and benchmarks

Yuze Han; Jialin Shi; Yuxuan Zhang

doi:10.1117/12.2641185

19 October 2022 Comparison on long-term tracking: methods and benchmarks

Yuze Han, Jialin Shi, Yuxuan Zhang

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122942T (2022) https://doi.org/10.1117/12.2641185
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

While dealing with transient target absence or sudden position change, short-term visual object tracking is not capable of continuously spotting the target. Therefore, long-term tracking methods are indispensable for handling these issues. It is necessary to summarize these long-term tracking methods. In this work, we propose to demonstrate some long-term tracking methods including LTMU, ST Transformer and GlobalTrack and three benchmarks (OxUvA, TLP, and CDTB), and we aim to compare these methods based on various benchmarks. All those methods achieve distinguishing performance on those datasets. Furthermore, we are going to present some prospects of the long-term tracking.

Citation Download Citation

Yuze Han, Jialin Shi, and Yuxuan Zhang "Comparison on long-term tracking: methods and benchmarks", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122942T (19 October 2022); https://doi.org/10.1117/12.2641185

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