Data-driven feature extraction is examined to realize accurate and robust calving detection. Automatic calving sign detection systems can support farmers' decision making. In this paper, neural networks are designed to extract information relevant to calving signs, which can be observed from video, such as the frequency in pre-calving postures, statistics in movement, and statistics in rotation. Experimental comparisons using surveillance video demonstrate that the proposed feature extraction methods contribute to reducing false positives and explaining the basis of the prediction compared to the end-to-end calving detection system.