2 Dimensional Skeleton

Two-dimensional (2D) skeleton representations are emerging as a powerful tool in various computer vision tasks, primarily focusing on human action recognition and segmentation in microscopy images. Current research emphasizes developing robust models, often employing temporal convolutional networks (TCNs) or graph convolutional networks (GCNs), to extract spatiotemporal features from 2D skeleton heatmaps, sometimes augmented with multi-modality fusion (e.g., combining with RGB video data). This approach offers advantages in handling noisy or incomplete data, leading to improved accuracy and robustness compared to methods relying solely on 3D skeletons. The resulting advancements have significant implications for applications such as assistive robotics, medical image analysis, and gait recognition.