A Change-Aware Approach for Relative Motion Segmentation | |
Zhuojun Zou2,3; Zhaoteng Meng2,3; Lin Shu3; Jie Hao1,3 | |
2021-07 | |
会议日期 | 2021.7.5 |
会议地点 | Shenzhen, China |
英文摘要 | Analysis on changes of image features is an effective means to leverage multi-dimensional information in motion segmentation. Methods without considering temporal perspective are agnostic of motion state, and inherently unsuitable for motion detection. However, the effect of existing spatio-temporal approaches is lagging behind that of spatial methods by a margin. To make better use of temporal information, this paper tackles the task of moving object segmentation by constructing a Change-aware Siamese neural network(ChaSiam) to detect relative foreground and changes. Further, a reference frame update strategy is attached to our network for overcoming the weakness of spatio-temporal approaches in cases with camera ego-motion. Extensive experiments show that our proposed model outperforms previous state-of-the-art spatio-temporal methods on Change Detection dataset, and compared with spatial methods our model has similar performance with better generalization. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/52271] |
专题 | 国家专用集成电路设计工程技术研究中心_实感计算 |
通讯作者 | Jie Hao |
作者单位 | 1.Guangdong Institute of Artificial Intelligence and Advanced Computing 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Zhuojun Zou,Zhaoteng Meng,Lin Shu,et al. A Change-Aware Approach for Relative Motion Segmentation[C]. 见:. Shenzhen, China. 2021.7.5. |
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