MULTI-STAGE VEHICLE DETECTION IN SIDE-BY-SIDE DRIVE-THRU CONFIGURATIONS

• US 20160342841A1
• Filed: 05/19/2015
• Published: 11/24/2016
• Est. Priority Date: 05/19/2015
• Status: Active Grant

• Current Assignee: Conduent Business Services, LLC (Conduent, Inc.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Bulan, Orhan, Burry, Aaron M.
• Granted Patent: US 10,044,988 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/41 Interactive pattern learning with a human teacher
• CPC Class Codes: G06Q 30/0635 Processing of requisition or of purchase orders
• CPC Class Codes: G06Q 50/12 Hotels or restaurants
• CPC Class Codes: G06V 10/7788 the supervisor being a human, e.g. interactive learning with a human teacher
• CPC Class Codes: G06V 20/54 of traffic, e.g. cars on the road, trains or boats
• CPC Class Codes: H04N 7/181 for receiving images from a plurality of remote sources

Abstract

Multi-stage vehicle detection systems and methods for side-by-side drive-thru configurations. One or more video cameras for an image-capturing unit) can be employed for capturing video of a drive-thru of interest in a monitored area. A group of modules can be provided, which define multiple virtual detection loops in the video and sequentially perform classification with respect to each virtual detection loops among the multiple virtual detection loops, starting from a virtual detection loop closest to an order point, and when a vehicle having a car ID is sitting in a drive-thru queue, so as to improve vehicle detection performance in automated post-merge sequencing.

Claims

1. A multi-stage vehicle detection system for side-by-side drive-thru configurations, said system comprising:

at least one video camera for capturing video of a drive-thru of interest in a monitored area; and

a plurality of modules that defines multiple virtual detection loops in said video and sequentially performs classification with respect to each virtual detection loop among said multiple virtual detection loops, starting from a virtual detection loop closest to an order point, and when a vehicle having a car ID is sitting in a drive-thru queue, so as to improve vehicle detection performance in automated post-merge sequencing.

2. The system of claim 1 wherein said plurality of modules comprises:

a video acquisition module including said at least one video camera that acquires said video of said drive-thru of interest;

a vehicle detection module that defines said multiple virtual detection loops in said video and sequential classification in said virtual detection loop to detect vehicles as they are assigned to said car ID at said order point;

a vehicle tracking module that tracks detected vehicles until said detected vehicles leave said monitored area; and

a sequence reporting module that reports an assigned car ID and originating lanes as vehicles proceed through a cash out point.

3. The system of claim 1 further comprising at least one classifier that performs said classification.

4. The system of claim 3 further comprising an offline stage wherein features of manually labeled samples of vehicles crossing said virtual detection loops are extracted and fed into said at least one classifier for training and wherein said at least one classifier after training is employed within said system.

5. The system of claim 3 wherein said at least one classifier comprises an order point classifier.

6. The system of claim 3 wherein said at least one classifier comprises a merge point classifier. The system of claim 2 further comprising:

at least one classifier; and

an offline stage wherein features of manually labeled samples of vehicles crossing said virtual detection loops are extracted and fed into said at least one classifier for training and wherein said at least one classifier after training is employed within said system.

12. The system of claim 1 wherein said instructions are further configured in an offline stage extracting and feeding manually labeled samples of vehicles crossing said virtual detection loops into said at least one classifier for training and wherein said at least one classifier after training is employed within said system.

8. The system of claim 7 wherein said at least one classifier comprises at least one of an order point classifier and a merge point classifier.

9. A multi-stage vehicle detection system for side-by-side drive-thru configurations, said system comprising:

at least one video camera;

a processor that communicates with said at least one video camera; and

a computer-usable medium embodying computer program code, said computer-usable medium capable of communicating with the processor, said computer program code comprising instructions executable by said processor and configured for;

capturing via said at least one video camera, video of a drive-thru of interest in a monitored area;

defining multiple virtual detection loops in said video; and

sequentially performing a classification with respect to each virtual detection loop among said multiple virtual detection loops, starting from a virtual detection loop closest to an order point, and when a vehicle having a car ID is sitting in a drive-thru queue, so as to improve vehicle detection performance in automated post-merge sequencing.

10. The system of claim 9 wherein further comprising:

a video acquisition module including said at least one video camera that acquires said video of said drive-thru of interest;

a vehicle detection module that defines said multiple virtual detection loops in said video and sequential classification in said virtual detection loop to detect vehicles as they are assigned to said car ID at said order point;

a vehicle tracking module that tracks detected vehicles until said detected vehicles leave said monitored area; and

a sequence reporting module that reports an assigned car ID and originating lanes as vehicles proceed through a cash out point.

11. The system of claim 9 further comprising at least one classifier that sequentially performs said classification.

13. The system of claim 11 wherein said at least one classifier comprises an order point classifier.

14. The system of claim 11 wherein said at least one classifier comprises a merge point classifier.

15. The system of claim 10 further comprising:

at least one classifier; and

an offline stage wherein features of manually labeled samples of vehicles crossing said virtual detection loops are extracted and fed into said at least one classifier for training and wherein said at least one classifier after training is employed within said system.

16. A multi-stage vehicle detection method for side-by-side drive-thru configurations, said method comprising:

capturing video of a drive-thru of interest in a monitored area utilizing at least one video camera; and

providing a plurality of modules that defines multiple virtual detection loops in said video and sequentially performs classification with respect to each virtual detection loop among said multiple virtual detection loops, starting from a virtual detection loop closest to an order point, and when a vehicle having a car ID is sitting in a drive-thru queue, so as to improve vehicle detection performance in automated post-merge sequencing.

17. The method of claim 16 wherein said plurality of modules comprises:

a video acquisition module including said at least one video camera that acquires said video of said drive-thru of interest;

a vehicle detection module that defines said multiple virtual detection loops in said video and sequential classification in said virtual detection loop to detect vehicles as they are assigned to said car ID at said order point;

a vehicle tracking module that tracks detected vehicles until said detected vehicles leave said monitored area; and

a sequence reporting module that reports an assigned car ID and originating lanes as vehicles proceed through a cash out point.

18. The method of claim 16 further comprising at least one classifier to perform said classification.

19. The method of claim 18 further comprising in an offline stage extracting and feeding said features of manually labeled samples of vehicles crossing said virtual detection loops into said at least one classifier for training and wherein said at least one classifier after training is employed within said system.

20. The method of claim 19 further comprising configuring said at least one classifier to function as at least one of:

an order point classifier and a merge point classifier.