Motorola Solutions, Inc.

Kangyan Liu

Chia Ying Lee

Aleksey Lipchin

Yanyan Hu

Yin Wang

Qifan Liang

Weijuan Wu

Ying Wang

Dung-Han Lee

Methods for detecting anomalies in a geographic area include receiving, from an electronic computing device, expected relationship data indicating expected relationships between a plurality of entities within the geographic area; detecting the plurality of entities within the geographic area; generating observed relationship data indicating observed relationships between the plurality of entities; identifying the expected relationships between the plurality of entities based on the expected relationship data; determining that a given observed relationship between two entities of the plurality of entities is likely to represent an anomaly based on the expected relationship data; and providing an electronic notification to a safety officer, the electronic notification indicating that the given observed relationship is likely to represent the anomaly.

Systems and methods for detecting anomalies in geographic areas

A method, system and computer program product for self-learned and probabilistic-based prediction of inter-camera object movement is disclosed. The method includes building and storing a transition model defined by transition probability and transition time distribution data generated during operation of a first video camera and one or more other video cameras over time. The method also includes employing at least one balance flow algorithm on the transition probability and transition time distribution data to determine a subset of the video cameras to initiate a search for an object based on a query. The method also includes running the search for the object over the subset of the video cameras.

Method, system and computer program product for self-learned and probabilistic-based prediction of inter-camera object movement

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Revere, MA, United States of America

Kangyan Liu