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- W4285310933 abstract "Object detection methods employing convolutional neural networks have fueled the development of automated behavior analysis, which is traditionally composed of two steps of data processing: tracking and behavior annotation. The former generates the time series of positional data describing the movement of individuals and/or body parts, which is subsequently analyzed in the latter to identify particular behaviors/actions using the mathematically defined criteria. YOLO is a state-of-the-art algorithm for high-speed object detection, which can be used for the tracking of animals in video images. In this section, we extend the use of YOLO for the annotation of various behaviors in a single step. To test the usability of this method, we analyzed the territorial and courtship behavior in a hyper-aggressive fruit fly, Drosophila prolongata. Automated behavior analysis using YOLO successfully identified when and where each fight and courtship event occurred, demonstrating that this YOLO-based behavior analysis system can serve as a versatile tool for behavioral studies because it is easy to handle for researchers who are not experts in computer science." @default.
- W4285310933 created "2022-07-14" @default.
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- W4285310933 date "2022-01-01" @default.
- W4285310933 modified "2023-10-02" @default.
- W4285310933 title "Automated Behavior Analysis Using a YOLO-Based Object Detection System" @default.
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- W4285310933 doi "https://doi.org/10.1007/978-1-0716-2321-3_14" @default.
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