Exploiting rank-based filter pruning for real-time UAV tracking

IF 3.4 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Xucheng Wang , Dan Zeng , Qijun Zhao , Shuiwang Li
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引用次数: 0

Abstract

UAV tracking is an emerging task and has wide potential applications in such as agriculture, navigation, entertainment and public security. However, the limitations of computing resources, battery capacity, and maximum load of UAV hinder the deployment of DL-based tracking algorithms on UAV. In contrast to deep learning trackers, discriminative correlation filters (DCF)-based trackers stand out in the UAV tracking community because of their high efficiency. However, their precision is usually much lower than trackers based on deep learning. Model compression is a promising way to reduce the disparity (i.e., efficiency, precision) between DCF- and deep learning- based trackers, which has not caught much attention in the UAV tracking community. In this paper, We propose the P-SiamFC++ tracker, which is the first to use rank-based filter pruning to compress the SiamFC++ model, achieving a remarkable balance between efficiency and precision. Our method is general and could inspire additional research into UAV tracking with model compression in the future. Extensive experiments on four UAV benchmarks, including UAV123@10fps, DTB70, UAVDT and Vistrone2018, show that P-SiamFC++ tracker significantly outperforms state-of-the-art UAV tracking methods.
利用基于秩的滤波剪枝实现无人机实时跟踪
无人机跟踪是一项新兴任务,在农业、导航、娱乐和公共安全等领域具有广泛的应用潜力。然而,无人机的计算资源、电池容量和最大负载的限制阻碍了基于dl的跟踪算法在无人机上的部署。与深度学习跟踪器相比,基于判别相关滤波器(DCF)的跟踪器以其高效率在无人机跟踪领域脱颖而出。然而,它们的精度通常比基于深度学习的跟踪器低得多。模型压缩是一种很有前途的方法,可以减少基于DCF和基于深度学习的跟踪器之间的差距(即效率和精度),但在无人机跟踪界并没有引起太多关注。在本文中,我们提出了p - siamfc++跟踪器,它是第一个使用基于秩的滤波器剪枝来压缩siamfc++模型的跟踪器,在效率和精度之间取得了显著的平衡。该方法具有一定的通用性,可以启发未来基于模型压缩的无人机跟踪研究。在UAV123@10fps、DTB70、UAVDT和Vistrone2018等四种无人机基准测试上进行的大量实验表明,p - siamfc++跟踪器明显优于最先进的无人机跟踪方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Signal Processing-Image Communication
Signal Processing-Image Communication 工程技术-工程:电子与电气
CiteScore
8.40
自引率
2.90%
发文量
138
审稿时长
5.2 months
期刊介绍: Signal Processing: Image Communication is an international journal for the development of the theory and practice of image communication. Its primary objectives are the following: To present a forum for the advancement of theory and practice of image communication. To stimulate cross-fertilization between areas similar in nature which have traditionally been separated, for example, various aspects of visual communications and information systems. To contribute to a rapid information exchange between the industrial and academic environments. The editorial policy and the technical content of the journal are the responsibility of the Editor-in-Chief, the Area Editors and the Advisory Editors. The Journal is self-supporting from subscription income and contains a minimum amount of advertisements. Advertisements are subject to the prior approval of the Editor-in-Chief. The journal welcomes contributions from every country in the world. Signal Processing: Image Communication publishes articles relating to aspects of the design, implementation and use of image communication systems. The journal features original research work, tutorial and review articles, and accounts of practical developments. Subjects of interest include image/video coding, 3D video representations and compression, 3D graphics and animation compression, HDTV and 3DTV systems, video adaptation, video over IP, peer-to-peer video networking, interactive visual communication, multi-user video conferencing, wireless video broadcasting and communication, visual surveillance, 2D and 3D image/video quality measures, pre/post processing, video restoration and super-resolution, multi-camera video analysis, motion analysis, content-based image/video indexing and retrieval, face and gesture processing, video synthesis, 2D and 3D image/video acquisition and display technologies, architectures for image/video processing and communication.
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