摘要
In this paper, the problem of moving object detection in aerial video is addressed. While motion cues have been extensively exploited in the literature, how to use spatial information is still an open problem. To deal with this issue, we propose a novel hierarchical moving target detection method based on spatiotemporal saliency. Temporal saliency is used to get a coarse segmentation, and spatial saliency is extracted to obtain the object＇s appearance details in candidate motion regions. Finally, by combining temporal and spatial saliency information, we can get refined detection results. Additionally, in order to give a full description of the object distribution, spatial saliency is detected in both pixel and region levels based on local contrast. Experiments conducted on the VIVID dataset show that the proposed method is efficient and accurate.
In this paper, the problem of moving object detection in aerial video is addressed. While motion cues have been extensively exploited in the literature, how to use spatial information is still an open problem. To deal with this issue, we propose a novel hierarchical moving target detection method based on spatiotemporal saliency. Temporal saliency is used to get a coarse segmentation, and spatial saliency is extracted to obtain the object＇s appearance details in candidate motion regions. Finally, by combining temporal and spatial saliency information, we can get refined detection results. Additionally, in order to give a full description of the object distribution, spatial saliency is detected in both pixel and region levels based on local contrast. Experiments conducted on the VIVID dataset show that the proposed method is efficient and accurate.
基金
co-supported by the National Natural Science Foundation of China (Nos.61005028,61175032,and 61101222)
作者简介
Shen Hao is a Ph.D. student in Institute of Automation, Chinese Academy of Sciences. He received his B.S. degree in mechanical engineering and automation from Hohai University in 2008. His main research interests are moving object detection and machine vision.Corresponding author. Tel.： ＋ 86 10 62550985 21. E-mail address： chengfei.zhu@ia.ac.cn （C. Zhu）.Zhu Chengfei is an assistant professor in Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree from Chinese Academy of Sciences in 2010. His main research interests are computer vision, object detection and recognition.Chang Hongxing is a professor with Institute of Automation, Chinese Academy of Sciences. He received his B.Sc. and M.Sc. degrees in mechanical engineering from Beijing University of Aeronautics and Astronautics, China, in 1986 and 1991, respectively. Presently, he is the director of Integrated Information System Research Center. His research interests include computer and machine vision, pattern rec- ognition, and intelligent UAV systems.
