Know Where You Are From: Event-Based Segmentation via Spatio-Temporal Propagation

Event cameras have gained attention in segmentation due to their higher temporal resolution and dynamic range compared to traditional cameras. However, they struggle with issues like lack of color perception and triggering only at motion edges, making it hard to distinguish objects with similar contours or segment spatially continuous objects. Our work aims to address these often overlooked issues. Based on the assumption that various objects exhibit different motion patterns, we believe that embedding the historical motion states of objects into segmented scenes can effectively address these challenges. Inspired by this, we propose the ESS framework ``Know Where You Are From" (KWYAF), which incorporates past motion cues through spatio-temporal propagation embedding. This framework features two core components: the Sequential Motion Encoding Module (SME) and the Event-Based Reliable Region Selection Mechanism (ER²SM). SMEs construct prior motion features through spatio-temporal correlation modeling for boosting final segmentation, while ER²SM adapts to identify high-confidence regions, embedding motion more precisely through local window masks and reliable region selection. A large number of experiments have demonstrated the effectiveness of our proposed framework in terms of both quantity and quality.

Authors

• Ke Li
• Gengyu Lyu College of Computer Science, Beijing University of Technology School of Computer Science and Technology, Beijing Jiaotong University Idealism Beijing Technology Co., Ltd.
• Hao Chen
• Bochen Xie City University of Hong Kong
• Zhen Yang
• Youfu Li City University of Hong Kong
• Yongjian Deng College of Computer Science, Beijing University of Technology

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