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Reference TypeConference Paper (Conference Proceedings)
Last Update2015: (UTC)
Metadata Last Update2020: (UTC) administrator
Citation KeyColqueJúniSchw:2015:HiOpFl
TitleHistograms of Optical Flow Orientation and Magnitude to Detect Anomalous Events in Videos
Access Date2022, Jan. 28
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Author1 Colque, Rensso Victor Hugo Mora
2 Júnior, Carlos Antônio Caetano
3 Schwartz, William Robson
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
3 Universidade Federal de Minas Gerais
EditorPapa, João Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2015-06-12 15:20:46 :: -> administrator ::
2020-02-19 02:14:02 :: administrator -> :: 2015
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Is the master or a copy?is the master
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Content TypeExternal Contribution
KeywordsAnomalous event detection
spatiotemporal feature extraction
optical flow
histograms of oriented optical flow
smart surveillance
AbstractModeling human behavior and activity patterns for recognition or detection of anomalous events has attracted significant research interest in recent years, particularly among the video surveillance community. An anomalous event might be characterized as an event that deviates from the normal or usual, but not necessarily in an undesirable manner, e.g., an anomalous event might just be different from normal but not a suspicious event from the surveillance stand point. One of the main challenges of detecting such events is the difficulty to create models due to their unpredictability. Therefore, most works model the expected patterns on the scene, instead, based on video sequences where anomalous events do not occur. Assuming images captured from a single camera, we propose a novel spatiotemporal feature descriptor, called emph{Histograms of Optical Flow Orientation and Magnitude} (HOFM), based on optical flow information to describe the normal patterns on the scene, so that we can employ a simple nearest neighbor search to identify whether a given unknown pattern should be classified as an anomalous event. Our descriptor captures spatiotemporal information from cuboids (regions with spatial and temporal support) and encodes both magnitude and orientation of the optical flow separately into histograms, differently from previous works, which are based only on the orientation. The experimental evaluation demonstrates that our approach is able to detect anomalous events with success, achieving better results than the descriptor based only on optical flow orientation and outperforming several state-of-the-art methods on one scenario (Peds2) of the well-known UCSD anomaly data set, and achieving comparable results in the other scenario (Peds1). > SDLA > SIBGRAPI 2015 > Histograms of Optical...
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