| 引用本文: | 陈昊天,张彪,孙凤池,黄亚楼,苑晶.基于分层词袋模型的室外环境增量式场景发现[J].控制理论与应用,2020,37(7):1471~1480.[点击复制] |
| CHEN Hao-tian,ZHANG Biao,SUN Feng-chi,HUANG Ya-lou,YUAN Jing.Incremental scene detection in outdoor environment based on hierarchical bag-of-words model[J].Control Theory and Technology,2020,37(7):1471~1480.[点击复制] |
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| 基于分层词袋模型的室外环境增量式场景发现 |
| Incremental scene detection in outdoor environment based on hierarchical bag-of-words model |
| 摘要点击 1866 全文点击 477 投稿时间：2019-08-17 修订日期：2019-11-25 |
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| DOI编号 10.7641/CTA.2020.90683 |
| 2020,37(7):1471-1480 |
| 中文关键词 室外环境 移动机器人 场景发现 无监督学习 词袋模型 |
| 英文关键词 outdoor environment mobile robots scene detection unsupervised learning bag-of-words model |
| 基金项目 国家自然科学基金(61873327) |
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| 中文摘要 |
| 场景理解是机器人在多样化环境中自主执行任务的前提, 而场景发现是场景理解的一个重要内容. 由于具
体场景在空间和时间上存在连续性, 可以假定移动机器人在某一段时间内处于同一场景, 并且属于同一场景的图像
序列的视觉观感是相似的, 因此提出无需先验知识的增量式室外场景发现, 通过分层词袋模型建立图像和场景的联
系, 使得场景发现过程更加类似人类认知模式. 对于机器人实时获取的每一副图像, 首先将其分块, 然后利用动态
聚类算法增量式地得到相应的低层词典, 并据此词典提取高层词袋模型特征, 接下来, 再用另一动态聚类算法增量
式地完成场景发现, 从而判断当前图像属于一个已经历场景, 或未经历场景, 直到发现新场景. 实验结果证明, 该方
法能够在没有先验知识的情况下有效完成自主场景发现. |
| 英文摘要 |
| For the purpose of autonomously carrying out tasks in diversified environments, robots are required to possess the ability of scene understanding, and scene detection is considered as one of its most important components. Due to the continuity of time and space in a specific scene, it is hypothesized that mobile robots remain in the same scene during one period of time, and the image sequences from the same scene share a similar visual appearance. Therefore, an incremental scene detection method that requires no prior knowledge is proposed. By establishing the connection between images and scenes through a hierarchical BoW (bag of words) model, our method makes scene detection more similar to human cognitive process. Firstly, every image that is captured by robots in real time is segmented into blocks. Secondly, a dynamic clustering algorithm is implemented to incrementally build the low-level dictionary, according to which features of the high-level BoW model are extracted. Then another dynamic clustering algorithm is implemented for incremental scene detection, so that the current image is classified as either an experienced scene, or an unexperienced scene, until a new scene is detected. Experimental results show that our method can effectively complete autonomous scene detection without prior knowledge. |
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