Xiaoyu Fang

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Content-based video copy detection over large corpus with complex transformations is important but challenging. It is not surprising that most existing methods fall short of either sufficient robustness to detect severely deformed copies or high accuracy to localize copy segments. In this paper, we propose a video copy detection approach which exploits(More)
Content-based copy detection (CBCD) over large corpus with complex transformations is important but challenging for video content analysis. To accomplish the TRECVid 2010 CBCD task, we've proposed a copy detection approach which exploits complementary visual/audio features and sequential pyramid matching (SPM). Several independent detectors first match(More)
In this paper, we describe our system for surveillance event detection task in TRECVid 2011. We focus on pair-wise events (e.g., PeopleMeet, PeopleSplitUp, Embrace) that need to explore the relationship between two active persons, and action-like events (e.g. ObjectPut and Pointing) that need to find the happenings of a per-son's action. Our team had(More)
In this paper, we describe our system for interactive and retrospective surveillance event detection task in TRECVid 2012. We focus on pair-wise events (e.g., PeopleMeet, PeopleSplitUp, Embrace) that need to explore the relationship between two active persons, and action-like events (e.g. ObjectPut, CellToEar, PersonRuns and Pointing) that need to find the(More)
BACKGROUND Temperature is an important factor determining the performance and stability of the anaerobic digestion process. However, the microorganism-regulated mechanisms of temperature effects on the performance of anaerobic digestion systems remain further elusive. To address this issue, we investigated the changes in composition, diversity and(More)
Event detection in crowded surveillance videos is a challenging yet important problem. In this paper, we present our eSur (Event detection system on SURveillance video) system , which is derived from TRECVid'12 surveillance tasks. Currently, eSur attempts to detect two categories of events: 1) pair-wise events (e.g., PeopleMeet, PeopleSplitUp and Embrace);(More)