Moving object detection in aerial video based on spatiotemporal saliency

Shen, Hao, Li, Shuxiao, Zhu, Chengfei, Chang, Hongxing, & Zhang, Jinglan (2013) Moving object detection in aerial video based on spatiotemporal saliency. Chinese Journal of Aeronautics, 26(5), pp. 1211-1217.

View at publisher


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.

Impact and interest:

12 citations in Scopus
Search Google Scholar™
8 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 69566
Item Type: Journal Article
Refereed: Yes
Keywords: Aerial video, Computer vision, Object detection, Saliency, Unmanned aerial vehicles
DOI: 10.1016/j.cja.2013.07.038
ISSN: 1000-9361
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 31 Mar 2014 02:29
Last Modified: 01 Apr 2014 00:02

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page