  Anomaly detection in video streams is a challenging problem because of the
scarcity of abnormal events and the difficulty of accurately annotating them.
To alleviate these issues, unsupervised learning-based prediction methods have
been previously applied. These approaches train the model with only normal
events and predict a future frame from a sequence of preceding frames by use of
encoder-decoder architectures so that they result in small prediction errors on
normal events but large errors on abnormal events. The architecture, however,
comes with the computational burden as some anomaly detection tasks require low
computational cost without sacrificing performance. In this paper,
Cross-Parallel Network (CPNet) for efficient anomaly detection is proposed here
to minimize computations without performance drops. It consists of N smaller
parallel U-Net, each of which is designed to handle a single input frame, to
make the calculations significantly more efficient. Additionally, an
inter-network shift module is incorporated to capture temporal relationships
among sequential frames to enable more accurate future predictions.The
quantitative results show that our model requires less computational cost than
the baseline U-Net while delivering equivalent performance in anomaly
detection.
