The rapid popularity of UAVs has encouraged the development of Anti-UAV technology. Infrared-detector-based visual tracking for UAVs provides an encouraging solution for Anti-UAVs. However, it still faces the problem of tracking instability caused by environmental thermal crossover and similar distractors. To address these issues, we propose a spatio-temporal-focused Siamese network for infrared UAV tracking, called STFTrack. This method employs a two-level target focusing strategy from global to local. First, a feature pyramid-based Siamese backbone is constructed to enhance the feature expression of infrared UAVs through cross-scale feature fusion. By combining template and motion features, we guide prior anchor boxes towards the suspicious region to enable adaptive search region selection, thus effectively suppressing background interference and generating high-quality candidates. Furthermore, we propose an instance-discriminative RCNN based on metric learning to focus on the target UAV among candidates. By measuring calculating the feature distance between the candidates and the template, it assists in discriminating the optimal target from the candidates, thus improving the discrimination of the proposed method to infrared UAV. Extensive experiments on the Anti-UAV dataset demonstrate that the proposed method achieves outstanding performance for infrared tracking, with 91.2% precision, 66.6% success rate, and 67.7% average overlap accuracy, and it exceeded the baseline algorithm by 2.3%, 2.7%, and 3.5%, respectively. The attribute-based evaluation demonstrates that the proposed method achieves robust tracking effects on challenging scenes such as fast motion, thermal crossover, and similar distractors. Evaluation on the LSOTB-TIR dataset shows that the proposed method reaches a precision of 77.2% and a success rate of 63.4%, outperforming other advanced trackers.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    STFTrack: Spatio-Temporal-Focused Siamese Network for Infrared UAV Tracking


    Beteiligte:
    Xueli Xie (Autor:in) / Jianxiang Xi (Autor:in) / Xiaogang Yang (Autor:in) / Ruitao Lu (Autor:in) / Wenxin Xia (Autor:in)


    Erscheinungsdatum :

    2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Spatial-Temporal Contextual Aggregation Siamese Network for UAV Tracking

    Qiqi Chen / Xuan Wang / Faxue Liu et al. | DOAJ | 2024

    Freier Zugriff

    SiamCTCA: Cross-Temporal Correlation Aggregation Siamese Network for UAV Tracking

    Qiaochu Wang / Faxue Liu / Bao Zhang et al. | DOAJ | 2025

    Freier Zugriff

    Lightweight Spatial-Temporal Contextual Aggregation Siamese Network for Unmanned Aerial Vehicle Tracking

    Qiqi Chen / Jinghong Liu / Faxue Liu et al. | DOAJ | 2024

    Freier Zugriff

    MPSiam: A Fast Multiplexing Siamese Tracking Network

    Li, Donghao / Shen, Ce / Hu, Jinxing et al. | TIBKAT | 2022


    MPSiam: A Fast Multiplexing Siamese Tracking Network

    Li, Donghao / Shen, Ce / Hu, Jinxing et al. | Springer Verlag | 2021