Anomalies Identification in Images from Security Video Cameras Using Mask R-CNN
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Undergraduate course
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Ieee-inst Electrical Electronics Engineers Inc
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Article
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Abstract
In this work we developed a system to identify anomalies in images from video security cameras in an urban environment. Initially people are detected in the images using Mask R-CNN. From the binary mask are extracted characteristics of the people so that the anomalies can be detected. In order to facial recognition we used Facial Landmarks so that the system knows the residents and authorized people avoiding the false anomalies. We considered four anomalies in this work: the act of jumping a wall, standing for a long time in front of the residence, walking thru the sidewalk several times and entering a place without permission.
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Keywords
Mask R-CNN, CNN, HOG, People characteristics extraction, Intrusion detection, Facial recognition
Language
English
Citation
Ieee Latin America Transactions. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 18, n. 3, p. 530-536, 2020.
