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

—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.