Anomalous activity recognition in videos

Systems and methods are provided for detecting one or more anomalous events in video. Histogram-based noise cleansing, higher-order deep convolutional neural network-based feature extraction, instance segmentation, instance summation, difference calculation, and normalization can be used. Human-in-l...

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Bibliographische Detailangaben
Hauptverfasser: Rishe, Naphtali D, Amini, Mohammadhadi, Ahmed, Khandaker Mamun
Format: Patent
Sprache:eng
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Beschreibung
Zusammenfassung:Systems and methods are provided for detecting one or more anomalous events in video. Histogram-based noise cleansing, higher-order deep convolutional neural network-based feature extraction, instance segmentation, instance summation, difference calculation, and normalization can be used. Human-in-loop systems and methods can facilitate human decisions for anomaly detection. The decision of an anomaly event can be made by, for example, the instance difference value(s).