  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) low
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 backgound. 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 initialized supervised learning.
