METHODS AND SYSTEMS FOR EXCLUDING INDIVIDUALS FROM RETAIL ANALYTICS

• US 20150235237A1
• Filed: 04/27/2015
• Published: 08/20/2015
• Est. Priority Date: 02/29/2012
• Status: Active Grant

• Current Assignee: RetailNext, Inc.
• Original Assignee: RetailNext, Inc.
• Inventors: Shaw, George, Bellipady, Kushel Rai
• Granted Patent: US 9,569,786 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01S 11/06 using intensity measurements
• CPC Class Codes: G01S 5/0294 Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
• CPC Class Codes: G01S 5/06 Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements G01S5/12 takes precedence
• CPC Class Codes: G01S 5/14 Determining absolute distances from a plurality of spaced points of known location
• CPC Class Codes: G06Q 10/0833 Tracking
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06T 7/246 using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06T 7/292 Multi-camera tracking
• CPC Class Codes: G06T 7/77 using statistical methods
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G06V 2201/10 Recognition assisted with metadata
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16
• CPC Class Codes: H04W 24/00 Supervisory, monitoring or testing arrangements
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

Systems and methods are described for excluding individuals from retail analytics. Pedestrian traffic within a predetermined area is monitored via a video sensor to determine a video entrance. The video entrance may correspond to entrances of the predetermined area by a first set of individuals. The pedestrian traffic may also be monitored via a plurality of wireless sensors to determine a wireless entrance. The wireless entrance may correspond to entrances by a second set of individuals. The video entrance and the wireless entrance are compared to determine if any of the entrances associated with a video timestamp match any of the entrances with a wireless timestamp. An overall entrance count corresponding to the number of customers entering the predetermined area is determined. Retail analytics excluding employees may subsequently be calculated using the overall entrance count.

Claims

1. A method comprising:

monitoring, via a video sensor, pedestrian traffic within a predetermined area, the monitoring via the video sensor taking place over a predetermined period of time;

determining, by a processor, a video entrance by the monitoring via the video sensor, the video entrance corresponding to entrances of the predetermined area by a first set of individuals during the predetermined period of time, each entrance of the predetermined area by the first set of individuals being associated with a video timestamp;

monitoring, via a plurality of wireless sensors, the pedestrian traffic within the predetermined area, the monitoring via the plurality of wireless sensors taking place over the predetermined period of time;

determining, by a processor, a wireless entrance by the monitoring via the plurality of wireless sensors, the wireless entrance corresponding to entrances of the predetermined area by a second set of individuals during the predetermined period of time, each entrance of the predetermined area by the second set of individuals being associated with a wireless timestamp;

comparing, by a processor, the video entrance and the wireless entrance to determine if any of the entrances associated with a video timestamp match any of the entrances with a wireless timestamp;

determining, by a processor, an overall entrance count corresponding to the number of customers entering the predetermined area, the overall entrance count including the video entrance subtracted by entrances where the video timestamp and the wireless timestamp match; and

calculating retail analytics by a processor using the overall entrance count, the retail analytics outputting data that is based on the number of customers in a retail space that includes the predetermined area at the predetermined period of time.

2. The method of claim 1, the monitoring via a plurality of wireless sensors being performed by:

providing each of the second set of individuals with a wireless tag;

tracking a direction of the wireless tag by the plurality of wireless sensors when the wireless tag enters the predetermined area, the direction being an entrance when the wireless tag is determined to be moving in a first range of directions towards the predetermined area and the direction being an exit when the wireless tag is determined to be moving in a second range of directions away from the predetermined area, the first range of directions being disjoint from the second range of directions; and

tracking an entrance time for each entrance, the entrance time being the time window in which the wireless tag is moving in the first range of directions.

3. The method of claim 1 wherein there are exactly two sensors, a first sensor being located near the predetermined area, and the second sensor being located inside a retail space.

4. The method of claim 3, wherein there are three or more sensors, a first sensor being located near the predetermined area, a second sensor being located inside a retail space, and a third sensor being located outside of the retail space.

5. The method of claim 3 wherein the first sensor is located at an entry point of the retail space, and the second sensor is within the retail space.

6. The method of claim 1 where the comparing includes comparing the video entrance with the wireless entrance during preselected time window, and identifying a match when a direction of the wireless entrance and a direction of the video entrance are substantially the same.

7. The method of claim 1 wherein comparing involves comparing positional data obtained both wirelessly and by video at the same time points to see if there is a match.

8. The method of claim 1, wherein there is a threshold for matching.

9. The method of claim 1, wherein the comparing includes using an error correction model.

10. The method of claim 1, further comprising calculating retail analytics by a processor based on a plurality of wireless entrances by a plurality of individuals from the second set and corresponding video entrances that match the plurality of wireless entrances.

11. The method of claim 1, the video sensor and the plurality of wireless sensors being situated at a specified location deep inside the retail space.

12. The method of claim 10, the video sensor and the plurality of wireless sensors being situated at an entryway to the retail space.

13. A method comprising:

monitoring, by a video sensor, pedestrian traffic within a predetermined area, the monitoring via the video sensor taking place over a predetermined period of time;

determining, by a processor, video paths for a plurality of individuals over a predetermined period of time, the determining video paths including determining, for each individual of the plurality of individuals, a corresponding path by the monitoring via the video sensor, each individual'"'"'s corresponding path comprising a plurality of locations at a plurality of timestamps, each timestamp being associated with a corresponding location;

monitoring, by at least three wireless sensors, the pedestrian traffic within the predetermined area, the monitoring by the at least three wireless sensors taking place over the predetermined period of time;

determining, by a processor, a wireless path of an individual by the monitoring by the at least three wireless sensors, the wireless path comprising a plurality of locations at a plurality of timestamps, each timestamp being associated with a corresponding location;

comparing, by a processor, the wireless path and the video paths for the plurality of individuals to see if a match exists;

excluding, by a processor, a video path matching the wireless path from a compiled set of customer path data comprising the video paths for the plurality of individuals over the predetermined period of time; and

calculating retail analytics using the compiled set of customer path data, the retail analytics outputting data that is related to the number of customers in the predetermined area during the predetermined period of time.

14. The method of claim 13, further comprising:

determining, by the processor, if the video paths for each individual of the plurality of individuals includes a dwell, the dwell being a portion of a path where the location is the same for a dwell period comprising a predetermined number of timestamps;

excluding, by a processor, a video path that is determined to have a dwell and that matches the wireless path, from a compiled set of customer dwells comprising the video paths for individuals that include a dwell during the predetermined period of time; and

calculating dwell retail analytics using the compiled set of customer dwells, the dwell retail analytics outputting data that is related to the location of the dwell for each individual whose corresponding video path includes a dwell.

15. The method of claim 13, further comprising computing employee retail analytics using a compiled set of employee path data comprising a plurality of video paths, including the video path matching the wireless path, from the video paths for the plurality of individuals over the predetermined period of time, the employee retail analytics outputting data that is related to the number of employees in the predetermined area during the predetermined period of time.

16. The method of claim 13, where the wireless path of the individual is determined using one of trilateration, Markov localization, and nearest neighbor localization to determine the location of the individual at each timestamp.

17. The method of claim 13, further comprising:

monitoring, by the video sensor, pedestrian traffic within a second predetermined area, the monitoring via the video sensor taking place over a second predetermined period of time;

determining, by a processor, video paths for a second plurality of individuals over the second predetermined period of time, the determining video paths including determining, for each individual of the second plurality of individuals, a corresponding path by the monitoring via the video sensor, each individual'"'"'s corresponding path comprising a plurality of locations at a plurality of timestamps, each timestamp being associated with a corresponding location;

monitoring, via the at least three wireless sensors, the pedestrian traffic within the second predetermined area, the monitoring via the plurality of wireless sensors taking place over the second predetermined period of time;

determining, by a processor, a wireless path of a second individual by the monitoring via the at least three wireless sensors, the wireless path comprising a plurality of locations at a plurality of timestamps, each timestamp being associated with a corresponding location;

comparing, by a processor, the wireless path of the second individual and the video paths for the second plurality of individuals to see if a match exists;

excluding, by a processor, a video path matching the wireless path of the second individual from a compiled second set of customer path data comprising the video paths for the second plurality of individuals over the second predetermined period of time; and

computing second retail analytics using the compiled second set of customer path data, the retail analytics outputting data that is correlated to the number of customers in the second predetermined area during the second predetermined period of time.

18. The method of claim 13, the comparing further comprising a trajectory-based comparison of the wireless path with each of the video paths.

19. A non-transitory computer readable storage medium having embodied thereon a program, the program being executable by a processor for performing a method for enhancing a signal, the method comprising:

monitoring, via a video sensor, pedestrian traffic within a predetermined area, the monitoring via the video sensor taking place over a predetermined period of time;

determining a video entrance by the monitoring via the video sensor, the video entrance corresponding to entrances of the predetermined area by a first set of individuals during the predetermined period of time, each entrance of the predetermined area by the first set of individuals being associated with a video timestamp;

monitoring, via a plurality of wireless sensors, the pedestrian traffic within the predetermined area, the monitoring via the plurality of wireless sensors taking place over the predetermined period of time;

determining a wireless entrance by the monitoring via the plurality of wireless sensors, the wireless entrance corresponding to entrances of the predetermined area by a second set of individuals during the predetermined period of time, each entrance of the predetermined area by the second set of individuals being associated with a wireless timestamp;

comparing the video entrance and the wireless entrance to determine if any of the entrances associated with a video timestamp match any of the entrances with a wireless timestamp;

determining an overall entrance count corresponding to the number of customers entering the predetermined area, the overall entrance count including the video entrance subtracted by entrances where the video timestamp and the wireless timestamp match; and

calculating retail analytics using the overall entrance count, the retail analytics outputting data that is based on the number of customers in a retail space that includes the predetermined area at the predetermined period of time.