文章基本信息

标题：Analyzing animal behavior via classifying each video frame using convolutional neural networks
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作者：Ulrich Stern ; Ruo He ; Chung-Hui Yang 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2015
卷号：5
期号：1
DOI：10.1038/srep14351
语种：English
出版社：Springer Nature
摘要：High-throughput analysis of animal behavior requires software to analyze videos. Such software analyzes each frame individually, detecting animals’ body parts. But the image analysis rarely attempts to recognize “behavioral states”—e.g., actions or facial expressions—directly from the image instead of using the detected body parts. Here, we show that convolutional neural networks (CNNs)—a machine learning approach that recently became the leading technique for object recognition, human pose estimation, and human action recognition—were able to recognize directly from images whether Drosophila were “on” (standing or walking) or “off” (not in physical contact with) egg-laying substrates for each frame of our videos. We used multiple nets and image transformations to optimize accuracy for our classification task, achieving a surprisingly low error rate of just 0.072%. Classifying one of our 8 h videos took less than 3 h using a fast GPU. The approach enabled uncovering a novel egg-laying-induced behavior modification in Drosophila . Furthermore, it should be readily applicable to other behavior analysis tasks.