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Automated Neighborhood Watch and Management Through Digital Imagery

IP.com Disclosure Number: IPCOM000239180D
Publication Date: 2014-Oct-20
Document File: 3 page(s) / 51K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is an automated Neighborhood Watch system that continuously monitors residential activity through video images and uses analytics to identify negative changes. The system then takes action based on whether the change is positive or negative, the duration of the change, and user preferences.

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Automated Neighborhood Watch and Management Through Digital Imagery

Neighborhood Watch groups monitor the residential streets for suspicious activities; however, the groups are not effective when the people on watch are not at home.

A system is needed to automatically monitor neighborhoods for suspicious activity.

The novel system automates Neighborhood Watch activities by continuously monitoring locations. Using video image data (e.g., traffic, security, personal, etc.), it provides real-time identification of changes to the characteristics of a neighborhood. The current state is compared to an earlier image to identify any activity that has not been previously performed. The system uses analytics to determine if the changes are improvements or problems.

Given the technical complexity involved in disaggregating images into distinct elements and comparing those elements to a database allowing for identification of changes in condition, the methodology described herein is not obvious.

The first major step in the automated Neighborhood Watch system is to determine any changes. The system uses analytics to determine if these are positive or negative changes. If the system identifies a negative change, then it compares the change to thresholds (either default or user-defined). A user can select the thresholds relative to the desire to monitor negative changes (e.g., acceptable duration for a broken window). If the change exceeds the threshold, then the system flags it as a significant change. This threshold comparison can take two forms:


 Simple deviations from an individual element's condition (e.g., a broken window in a neighborhood that typically does not have broken windows)


 The duration of a deviation in a single element (e.g., a window identified as broken, with that window remaining broken for an extended period, perhaps for weeks)

If a negative change exceeds the threshold, then the system initiates tracking and monitors the change until it is resolved.

The second major step in the automated Neighborhood Watch system is to manage the existing negative changes. One or more activities can be performed to address the negative change. If problems are not resolved within a default or user-specified (optional) period, then the system notifies the appropriate authorities (e...