Shoppertrak Rct Corporation

Sensormatic Electronics Corporation

Gary Dispensa

William C Kastilahn

Edward A Marcheselli

Thomas D Stemen

Zhiqian Wang

Ian Harris

Eric Guanlao

Dave McNeill

Anurag Pande

Jim Martin

Paige Stover

Techniques for distinguishing objects (e.g., an individual or an individual pushing a shopping cart) are disclosed. An object is detected in images of a scene. A height map is generated from the images, and the object is represented as height values in the height map. Based on height properties associated with another object, it is determined whether the other object is associated with the object. If so determined, the objects are classified separately.

Techniques for automatically identifying secondary objects in a stereo-optical counting system

A method for counting and tracking defined objects includes the step of receiving subset data with a data capturing device, wherein the subset data is associated with defined objects and includes a unique identifier, an entry time, an exit time, and location data for each defined object. The method further includes the steps of receiving subset data at a counting system, counting the defined objects, tracking the defined objects, associating a location of a defined object with a predefined area, and/or generating path data by plotting X and Y coordinates for the defined object within the predefined area at sequential time periods.

System and method for detecting, tracking, and counting human objects of interest using a counting system and a data capture device

System and method for detecting, tracking and counting human objects of interest using a counting system and a data capture device

A system for counting and tracking objects of interest within a predefined area with a sensor that captures object data and a data capturing device that receives subset data to produce reports that provide information related to a time, geographic, behavioral, or demographic dimension.

A method and system for distributing labor based upon determinations of traffic in a facility, such as a store. Daily traffic forecast information is obtained for each day within a given time period from a source of such information. Calendar and event information for the facility for each day within the given time period is determined. Baseline days are selected from historical traffic data, and baseline averages and percentages for a predetermined time interval are also selected. The distribution of traffic for each day within the given time period at each time interval is determined using the baseline percentages for each time interval. Labor data is distributed. Labor recommendations are provided at each time interval for the given time period based on the distribution of traffic for each day, and at least one of other user-defined workforce requirements. The results of any or all the these steps are displayed to a user.

Traffic based labor allocation method and system

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Naperville, IL, United States of America

Gary Dispensa