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Remote Sens. 2015, 7(6), 6932-6949; doi:10.3390/rs70606932

Diverse Scene Stitching from a Large-Scale Aerial Video Dataset

1
Shaanxi Provincial Key Lab of Speech and Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi'an, China
2
School of Telecommunications Engineering, Xidian University, Xi'an, China
3
Newark, NJ 19711, USA" to "Newark, DE 19711, USA
*
Authors to whom correspondence should be addressed.
Academic Editors: Devrim Akca and Prasad S. Thenkabail
Received: 19 March 2015 / Revised: 19 May 2015 / Accepted: 22 May 2015 / Published: 28 May 2015
View Full-Text   |   Download PDF [14092 KB, uploaded 29 May 2015]   |  

Abstract

Diverse scene stitching is a challenging task in aerial video surveillance. This paper presents a hybrid stitching method based on the observation that aerial videos captured in real surveillance settings are neither totally ordered nor completely unordered. Often, human operators apply continuous monitoring of the drone to revisit the same area of interest. This monitoring mechanism yields to multiple short, successive video clips that overlap in either time or space. We exploit this property and treat the aerial image stitching problem as temporal sequential grouping and spatial cross-group retrieval. We develop an effective graph-based framework that can robustly conduct the grouping, retrieval and stitching tasks. To evaluate the proposed approach, we experiment on the large-scale VIRATaerial surveillance dataset, which is challenging for its heterogeneity in image quality and diversity of the scene. Quantitative and qualitative comparisons with state-of-the-art algorithms show the efficiency and robustness of our technique. View Full-Text
Keywords: diverse scene stitching; cross-group retrieval; aerial image stitching; aerial video surveillance diverse scene stitching; cross-group retrieval; aerial image stitching; aerial video surveillance
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yang, T.; Li, J.; Yu, J.; Wang, S.; Zhang, Y. Diverse Scene Stitching from a Large-Scale Aerial Video Dataset. Remote Sens. 2015, 7, 6932-6949.

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