Human Anomaly Detection

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dc.contributor.advisorDr. Mohammad Ashrafuzzaman Khan Assistant
dc.contributor.authorSheikh Faiyaz Ahmed
dc.contributor.authorFaten Almee Spondon
dc.contributor.authorAchuyat Saha Joy
dc.contributor.authorAhmed Sajid Imtiaz
dc.contributor.id1511081042
dc.contributor.id1510599042
dc.contributor.id1510607042
dc.contributor.id1510600042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2024-05-30
dc.date.accessioned2024-05-30T06:33:44Z
dc.date.available2024-05-30T06:33:44Z
dc.date.issued2019
dc.description.abstractSurveillance videos are able to capture a variety of realistic anomalies. In this report, we propose to learn anomalies by exploiting both normal and anomalous videos. To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i.e. the training labels (anomalous or normal) are at video level instead of clip-level. In our approach, we consider normal and anomalous videos as bags and video segments as instances in multiple instance learning (MIL), and automatically learn a deep anomaly ranking model that predicts high anomaly scores for anomalous video segments. Furthermore, we introduce sparsity and temporal smoothness constraints in the ranking loss function to better localize anomaly during training. We have used real-world surveillance videos, with 13 realistic anomalies such as fighting, road accident, burglary, robbery, etc. as well as normal activities. This dataset can be used for general anomaly detection considering all anomalies in one group and all normal activities in another group. Our experimental results show that our MIL method for anomaly detection achieves significant improvement on anomaly detection performance as compared to the state-of-the-art approaches. The low recognition performance of these baselines reveals that our dataset is very challenging and opens more opportunities for future work.
dc.description.degreeUndergraduate
dc.identifier.cd600000081
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/856
dc.language.isoen_US
dc.publisherNorth South University
dc.rights© NSU Library
dc.subjectTECHNOLOGY::Electrical engineering, electronics and photonics::Electrical engineering
dc.titleHuman Anomaly Detection
dc.typeProject
oaire.citation.endPage30
oaire.citation.startPage1
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