  We have designed a deep multi-stream network for automatically detecting
calving signs from video. Calving sign detection from a camera, which is a
non-contact sensor, is expected to enable more efficient livestock management.
As large-scale, well-developed data cannot generally be assumed when
establishing calving detection systems, the basis for making the prediction
needs to be presented to farmers during operation, so black-box modeling (also
known as end-to-end modeling) is not appropriate. For practical operation of
calving detection systems, the present study aims to incorporate expert
knowledge into a deep neural network. To this end, we propose a multi-stream
calving sign detection network in which multiple calving-related features are
extracted from the corresponding feature extraction networks designed for each
attribute with different characteristics, such as a cow's posture, rotation,
and movement, known as calving signs, and are then integrated appropriately
depending on the cow's situation. Experimental comparisons conducted using
videos of 15 cows demonstrated that our multi-stream system yielded a
significant improvement over the end-to-end system, and the multi-stream
architecture significantly contributed to a reduction in detection errors. In
addition, the distinctive mixture weights we observed helped provide
interpretability of the system's behavior.
