| 摘要: |
| 事件相机因其生物视觉的启发渊源，打破了计算机视觉领域的常规数据获取方式，直击计算机视觉领域中RGB图像的痛点，带来了二维图像传感器无法比拟的优势，引起了广大研究者的密切关注.事件相机带来去除冗余信息、快速感知能力、高动态范围的感光能力和低功耗特性等优势的同时，其异步的事件数据无法直接应用于现有的计算机视觉处理模式.因此，利用基于关键事件点的分类方法对事件相机的数据流进行分类.该方法检测带有重要信息的角点事件，并只对角点事件进行特征提取.在保留事件重要特征和凝练提取事件流特征的同时，有效地减少了对其他事件的运算量.对预设手势进行识别，以此验证该方法的有效性，实现了97.86%的准确率. |
| 关键词: 事件相机 关键目标 异步数据流 目标分类 角点事件 |
| DOI： |
| 分类号: |
| 基金项目:高分辨率对地观测系统重大专项青年创新基金；装备预研教育部联合基金（6141A020336） |
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| Research on Key Object Extraction and Classification in Asynchronous Data Stream |
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ZHANG Shu, DU Cong-Yang, WU Jin-Jian, SHI Guang-Ming, XIE Xue-Mei
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School of Artificial Intelligence, Xidian University, Xi'an 710071, China
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| Abstract: |
| Event camera has attracted the attention of the majority of researchers due to the inspiration of biological vision, breaks the way of regular data acquisition in the field of computer vision, directly hits the pain point of RGB images, and brings the advantages that 2D image sensors cannot match. Event Camera brings the advantages of removing redundant information, fast sensing capability, high dynamic range sensitivity and low power consumption, while its asynchronous event data cannot be directly applied to existing computer vision processing modes. Therefore, this paper classify the data stream using the key event based classification method. This method detects corner events with important information and only extracts features of corner events. While retaining the important features of event and condensing the extraction of event stream features, the amount of computation for other events is effectively reduced. The preset gesture is recognized to verify the validity of this method, achieving an accuracy of 97.86%. |
| Key words: event camera key object asynchronous data stream object classification corner event |
