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- W3027622974 abstract "Detecting change is part of human nature. Humans build up virtual images of their surroundings over time and notice any change instantly which all happens thanks to their brain. While all of this is functions very well in the human brain, there are substantial benefits to an efficient change detection mechanism, but then created virtually. To replicate the structural and algorithmic properties of the human brain, this paper presents a system based on Hierarchal Temporal Memory (HTM) to detect change in a series of images. Moreover, it integrates the change-detecting HTM machine into a robotic security guard, responsible for monitoring and guarding valuable items. The system was designed to serve a security surveillance application to process and analyse several image inputs during non-working hours and identifying changes e.g. related to unauthorised access. The input to the system is a series of multiple images that were initially processed with MATLAB in order to resizing them from a 256*256 matrix to a smaller size, to then convert them into a single line vector in CSV format to suit HTM’s input requirements. In the various tests being performed, it is shown that HTM detects spatial as well as temporal anomalies, when they occur once or twice, however, if the pattern occurs more regularly, then it gets learned and becomes a familiar pattern, meaning it is no longer detected as an anomaly." @default.
- W3027622974 created "2020-05-29" @default.
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- W3027622974 date "2019-01-21" @default.
- W3027622974 modified "2023-09-27" @default.
- W3027622974 title "Detecting Change using HTM for a Security Surveillance Application" @default.
- W3027622974 hasPublicationYear "2019" @default.
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