步入式混合现实空间人体姿态识别RGB数据
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适用于教育培训、应急管理、安全生产与地产建筑等领域。通过采集高质量RGB数据并结合深度学习技术，实现精准动作捕捉与投影匹配，呈现裸眼3D无穿戴交互体验。数据采集要求光线充足、背景简洁、设备固定；算法可实时提取人体骨骼关键点，支持虚拟实验、沉浸式教学、应急演练、实时监控及建筑展示。目标用户包括教育机构、培训中心、应急管理部门、安全监管机构和地产设计营销单位，有效解决穿戴设备依赖、交互延迟等问题，提升互动体验与效率。1.数据采集：运用自研设备在混合现实场景中自主拍摄并采集连续帧RGB 图像。采集过程严格遵循相关法规与安全标准，确保数据来源合法合规，且不涉及侵犯个人隐私等问题。 2.人体目标检测：采用自训练且经过优化的目标检测算法，针对每帧图像开展人体目标定位工作。算法基于大量真实场景数据进行训练与优化，能够有效适应混合现实场景中的复杂环境。该算法输出带有置信度的人体外接矩形框，其坐标表示为（x, y），便于后续精准提取人体区域。 3.关键点回归：利用自训练的多个姿态估计神经网络进行集成，将人体框内的ROI 图像作为输入。这些神经网络针对特定场景经过精心设计与训练，能够充分发挥各自优势，实现对人体17 个关键点（如肩部、肘部、腕部、髋部等）的精准定位，同时输出各关键点的像素坐标和置信度，为后续的指标分析提供准确的数据基础。 4.指标分析：依据连续帧的推理结果，对各关节在帧间的加速度进行计算。同时，结合关键点的置信度进行综合筛选，从而精准地识别出可能存在误差的帧，为后续的人工修正提供依据。 5.人工修正：对于加速度大于预设加速度阈值的异常帧，以及关键点置信度小于可靠阈值的单帧，安排专业人员进行人工干预。操作人员借助标注工具，手动调整错误的关键点坐标，确保数据的准确性。在人工修正过程中，详细记录修正操作，以便后续追溯与审核。 6.平滑处理：采用自训练的时空神经网络进行姿态平滑处理，对修正后的关键点序列进行优化。该网络充分考虑了时间和空间维度的信息，能够有效降低相邻帧之间的加速度误差，使关键点序列更加平滑自然，提升最终输出的人体姿态数据的质量。
Applicable to fields including education and training, emergency management, work safety, real estate and construction. By collecting high-quality RGB data combined with deep learning technology, it realizes precise motion capture and projection matching, presenting a glasses-free 3D wearable-free interactive experience. The data collection requires sufficient lighting, simple background and fixed equipment. The algorithm can extract human skeletal key points in real time, supporting virtual experiments, immersive teaching, emergency drills, real-time monitoring and architectural display. The target users include educational institutions, training centers, emergency management departments, safety supervision agencies and real estate design and marketing units, which effectively solves the problems of dependence on wearable devices and interaction delay, and improves interactive experience and efficiency. 1. Data Collection: Use self-developed equipment to autonomously shoot and collect continuous-frame RGB images in mixed reality scenarios. The collection process strictly follows relevant laws, regulations and safety standards to ensure the legality and compliance of data sources, and does not involve issues such as infringement of personal privacy. 2. Human Object Detection: Adopt a self-trained and optimized object detection algorithm to perform human object localization on each frame of image. The algorithm is trained and optimized based on a large amount of real scene data, and can effectively adapt to complex environments in mixed reality scenarios. The algorithm outputs human bounding boxes with confidence, whose coordinates are represented as (x, y), which facilitates the accurate extraction of human regions in subsequent steps. 3. Key Point Regression: Use an ensemble of self-trained multiple pose estimation neural networks, taking the ROI image within the human bounding box as input. These neural networks are carefully designed and trained for specific scenarios, leveraging their respective advantages to accurately locate 17 human key points (such as shoulders, elbows, wrists, hips, etc.), while outputting the pixel coordinates and confidence of each key point, providing an accurate data basis for subsequent indicator analysis. 4. Indicator Analysis: Calculate the inter-frame acceleration of each joint based on the inference results of continuous frames. At the same time, conduct comprehensive screening combined with the confidence of key points to accurately identify frames that may have errors, providing a basis for subsequent manual correction. 5. Manual Correction: For abnormal frames with acceleration exceeding the preset acceleration threshold, and single frames with key point confidence lower than the reliable threshold, arrange professional personnel to perform manual intervention. Operators use annotation tools to manually adjust incorrect key point coordinates to ensure data accuracy. During the manual correction process, detailed records of correction operations are made for subsequent traceability and review. 6. Smoothing Processing: Use a self-trained spatiotemporal neural network for pose smoothing processing to optimize the corrected key point sequence. The network fully considers information in both temporal and spatial dimensions, effectively reducing the acceleration error between adjacent frames, making the key point sequence smoother and more natural, and improving the quality of the final output human pose data.
