Segmentation of Human Body Parts using Growing Neural Gas with Event Camera

Event cameras offer superior performance in terms of temporal resolution, dynamic range, latency, and power efficiency compared to conventional cameras. However, due to their unique operational principles, which involve measuring changes in luminance values, tasks such as human pose estimation become more challenging. In this study, we propose a real-time feature extraction method from event data for human pose estimation using event cameras. Specifically, after adjusting resolutions with Multi-resolution Maps, we employ Growing Neural Gas for topological clustering. Subsequently, spectral clustering is performed based on the resulting graph structure, enabling the segmentation of human body parts with a focus on the graph structure.