arXiv:1909.13868
Deep learning tools for the measurement of animal behavior in neuroscience
원문 초록
Recent advances in computer vision have made accurate, fast and robust measurement of animal behavior a reality. In the past years powerful tools specifically designed to aid the measurement of behavior have come to fruition. Here we discuss how capturing the postures of animals - pose estimation - has been rapidly advancing with new deep learning methods. While challenges still remain, we envision that the fast-paced development of new deep learning tools will rapidly change the landscape of realizable real-world neuroscience.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
DeepSTEP: Simultaneous Tracking and Estimation of Pose for Animals and Humans
SenseShift6D: Multimodal RGB-D Benchmarking for Robust 6D Pose Estimation across Environment and Sensor Variations
Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth Estimation
SpatialTrackerV2: 3D Point Tracking Made Easy
GKNet: Graph-based Keypoints Network for Monocular Pose Estimation of Non-cooperative Spacecraft
VST-Pose: A Velocity-Integrated Spatiotem-poral Attention Network for Human WiFi Pose Estimation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.