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- W4313444738 abstract "Nowadays surveillance cameras are everywhere and it produce a tremendous amount of data every day, making manual detection of abnormal activities a laborious one. So, various automatic abnormal human activities prediction techniques have been proposed. Rain Streaks Removal—Enhanced Object Detection and Tracking—Spatio-temporal Frequent Object Mining (RSR-EODT-STFOM) predicted the abnormal human activity in surveillance videos; STFOM-Face Detection-Pose-Invariant Orthogonal Locality Preserving Projection (STFOM-FD-PIOLPP) recognized face of those persons. In this paper, the STFOM-FD-PIOLPP is extended by detecting the age and gender of the unknown faces. Stacked Trimmed Active Shape Model (STASM) is introduced to locate facial landmarks for efficient face recognition. Speeded Up Robust Features (SURF), OLPP, HOG, Weighted LBP, HFO, and HFD are used to classify the age and gender of the unknown faces using CNN. Estimated age and gender of unknown faces are saved in database for further clarification of human abnormal activity in surveillance videos." @default.
- W4313444738 created "2023-01-06" @default.
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- W4313444738 date "2023-01-01" @default.
- W4313444738 modified "2023-10-16" @default.
- W4313444738 title "A Novel Approach for Age and Gender Estimation in Surveillance Videos" @default.
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- W4313444738 doi "https://doi.org/10.1007/978-981-19-2358-6_30" @default.
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