21 May 2015 Object and activity detection from aerial video
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Abstract
Aerial video surveillance has advanced significantly in recent years, as inexpensive high-quality video cameras and airborne platforms are becoming more readily available. Video has become an indispensable part of military operations and is now becoming increasingly valuable in the civil and paramilitary sectors. Such surveillance capabilities are useful for battlefield intelligence and reconnaissance as well as monitoring major events, border control and critical infrastructure. However, monitoring this growing flood of video data requires significant effort from increasingly large numbers of video analysts. We have developed a suite of aerial video exploitation tools that can alleviate mundane monitoring from the analysts, by detecting and alerting objects and activities that require analysts’ attention. These tools can be used for both tactical applications and post-mission analytics so that the video data can be exploited more efficiently and timely. A feature-based approach and a pixel-based approach have been developed for Video Moving Target Indicator (VMTI) to detect moving objects at real-time in aerial video. Such moving objects can then be classified by a person detector algorithm which was trained with representative aerial data. We have also developed an activity detection tool that can detect activities of interests in aerial video, such as person-vehicle interaction. We have implemented a flexible framework so that new processing modules can be added easily. The Graphical User Interface (GUI) allows the user to configure the processing pipeline at run-time to evaluate different algorithms and parameters. Promising experimental results have been obtained using these tools and an evaluation has been carried out to characterize their performance.
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Stephen Se, Feng Shi, Xin Liu, Mohsen Ghazel, "Object and activity detection from aerial video", Proc. SPIE 9473, Geospatial Informatics, Fusion, and Motion Video Analytics V, 947305 (21 May 2015); doi: 10.1117/12.2177094; https://doi.org/10.1117/12.2177094
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