Video surveillance is gaining increasing popularity to assist in railway intrusion detection in recent years. However, efficient and accurate intrusion detection remains a challenging issue due to: (a) limited sample number: only small sample size (or portion) of intrusive video frames is available; (b) high inter-scene dissimilarity: various railway track area scenes are captured by cameras installed in different landforms; (c) high intra-scene similarity: the video frames captured by an individual camera share a same background. In this paper, an efficient few-shot learning solution is developed to address the above issues. In particular, an enhanced model-agnostic meta-learner is trained using both the original video frames and segmented masks of track area extracted from the video. Moreover, theoretical analysis and engineering solutions are provided to cope with the highly similar video frames in the meta-model training phase. The proposed method is tested on realistic railway video dataset. Numerical results show that the enhanced meta-learner successfully adapts unseen scene with only few newly collected video frame samples, and its intrusion detection accuracy outperforms that of the standard randomly initialised supervised learning.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Enhanced Few-Shot Learning for Intrusion Detection in Railway Video Surveillance


    Contributors:
    Gong, Xiao (author) / Chen, Xi (author) / Zhong, Zhangdui (author) / Chen, Wei (author)


    Publication date :

    2022-08-01


    Size :

    3753186 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    A Railway Intrusion Detection Framework Based on Vehicle Front Video

    Cao, Zhiwei / Qin, Yong / Xie, Zhengyu et al. | Springer Verlag | 2022


    A Railway Intrusion Detection Framework Based on Vehicle Front Video

    Cao, Zhiwei / Qin, Yong / Xie, Zhengyu et al. | British Library Conference Proceedings | 2022



    Railway Foreign Object Intrusion Detection based on Deep Learning

    Ding, Xuewen / Cai, Xinnan / Zhang, Ziyi et al. | IEEE | 2022


    Ground Surveillance Radar for Perimeter Intrusion Detection

    Barry, A. S. / IEEE / AIAA | British Library Conference Proceedings | 2000