Pedestrian counting and detection at a traffic intersection based on location of vehicle zones

• US 9,449,506 B1
• Filed: 05/09/2016
• Issued: 09/20/2016
• Est. Priority Date: 05/09/2016
• Status: Active Grant

• Current Assignee: Iteris Incorporated
• Original Assignee: Iteris Incorporated
• Inventors: Whiting, Michael T., Gao, Yan, Swaminathan, Dilip, Shivakumar, Shashank Jayaram, Hwang, Robert J., Kreter, Todd W.
• Primary Examiner(s): Previl, Daniel
• Application Number:
  US15/150,258
• Time in Patent Office: 134 Days
• Field of Search: 340/944, 340/907, 340/916, 340/925, 340901-903, 340/917, 340/905, 340/933, 340/906, 340/540, 340/931, 340/425.5, 340/435
• US Class Current: 1/1
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06T 2207/30196 Human being; Person
• CPC Class Codes: G06T 2207/30236 Traffic on road, railway or crossing
• CPC Class Codes: G06T 2207/30242 Counting objects in image
• CPC Class Codes: G06T 7/215 Motion-based segmentation
• CPC Class Codes: G06V 10/25 Determination of region of interest [ROI] or a volume of interest [VOI]
• CPC Class Codes: G06V 10/50 by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• 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 40/103 Static body considered as a whole, e.g. static pedestrian or occupant recognition
• CPC Class Codes: G08G 1/04 using optical or ultrasonic detectors

Abstract

Pedestrian detection and counting for traffic intersection control analyzes characteristics of a field of view of a traffic detection zone to determine a location and size of a pedestrian area, and applies protocols for evaluating pixel content in the field of view to identify individual pedestrians. The location and size of a pedestrian area is determined based either on locations of vehicle and bicycle detection areas or on movement of various objects within the field of view. Automatic pedestrian speed calibration with a region of interest for pedestrian detection is accomplished using lane and other intersection markings in the field of view. Detection and counting further includes identifying a presence, volume, velocity and trajectory of pedestrians in the pedestrian area of the traffic detection zone.

Claims

1. A method, comprising:

receiving input data representing a field of view of a traffic detection zone;

analyzing the input data within a computing environment in one or more data processing modules executed in conjunction with at least one specifically-configured processor, the one or more data processing modules configured to a) identify a region in the field of view of the traffic detection zone used by one or more pedestrians, and b) accurately count the one or more pedestrians in the traffic detection zone, by

1) determining a pedestrian zone in the field of view, by a) identifying a position of at least one vehicle detection zone in nearest proximity to a stop bar, each at least one vehicle detection zone having a height that extends to or near to the stop bar, and b) calculating a height of a pedestrian area in the field of view from the height of the at least one vehicle detection zone;

2) counting the one or more pedestrians in the pedestrian zone by, a) analyzing portions of the pedestrian zone in the field of view, b) computing features of current pixel content identified in the analyzed portions by identifying part-based features defining an individual pedestrian that include one or more of body structure combinations, body shape, body width or walking gestures, c) developing a model of a single walking pedestrian to separate each individual pedestrian in a group of moving pedestrians in the field of view, by computing pedestrian features using pixels defining a pedestrian contour, d) determining a matching confidence between an individual pedestrian and a group of moving pedestrians by calculating a mathematical similarity between the computed features of current pixel content and the model of the single walking pedestrian, and e) incrementing a pedestrian count where a high matching confidence indicates that an individual pedestrian has been identified; and

generating, as output data, the pedestrian count.

2. The method of claim 1, wherein the determining a matching confidence between an individual pedestrian and a group of moving pedestrians further comprises identifying an individual pedestrian where a matching confidence is high, and analyzing a next portion of the field of view where a matching confidence is low.

3. The method of claim 1, wherein the identifying part-based features defining an individual pedestrian further comprises one or more of determining a width and a height of one or more object parts, comparing body structure combinations with one or more predetermined templates, and applying one or more geometric constraints to separate the part-based features.

4. The method of claim 1, wherein the determining a pedestrian zone in the traffic detection area further comprises extending a length of the pedestrian area to a leftmost edge of the field of view, and a rightmost edge of the field of view, and extending the height of the pedestrian area into a portion of the at least one vehicle detection zone.

5. The method of claim 1, further comprising applying a vehicle detection status to adjust sensitivity for detecting the one or more pedestrians in the pedestrian zone, the vehicle detection status including one or more of vehicular speed and traffic detection zone saturation.

6. The method of claim 5, further comprising dynamically changing a sensitivity for detecting the one or more pedestrians in the pedestrian zone by one or both of increasing a likelihood of a pedestrian crossing when a stopped vehicle is detected or no vehicle in present, and decreasing a likelihood of a pedestrian crossing while the vehicle traffic is free flowing.

7. The method of claim 1, wherein the input data is captured by one or more sensors in or near a traffic intersection, the one or more sensors including at least one of video cameras, a radar system, and a magnetometer.

8. The method of claim 1, further comprising identifying one or more pedestrian characteristics in the traffic detection zone, that at least include a pedestrian presence, a pedestrian volume, a pedestrian velocity, and a pedestrian trajectory.

9. The method of claim 1, further comprising providing the output data to a traffic management tool configured to enable a user to select a size and location of one or more of the field of view, the vehicle detection zone, and the stop bar.

10. A method of pedestrian detection and counting for traffic intersection control, comprising:

defining a position and size of a pedestrian zone in a traffic detection zone from locations of at least one vehicle detection zone relative to a stop bar within a field of view of the traffic detection zone, by a) calculating a height of a pedestrian area for the field of view from a height of the at least one vehicle detection zone, each at least one vehicle detection zone having a height that extends to or near to the stop bar, b) extending a length of the pedestrian area to a leftmost edge of the field of view, and a rightmost edge of the field of view, and c) extending the height of the pedestrian area into a portion of the at least one vehicle detection zone; and

detecting one or more pedestrians in the pedestrian zone from similarities of a model of a single walking pedestrian with part-based object recognition of individual pedestrians, by a) computing features of current pixel content identified analyzing portions of the pedestrian zone in the field of view to define individual pedestrians from the part-based object recognition of one or more of body structure combinations, body shape, body width or walking gestures, c) computing pedestrian features using pixels defining a pedestrian contour to develop the model of the single walking pedestrian to separate each individual pedestrian from a group of moving pedestrians in the field of view, d) determining a matching confidence between an individual pedestrian and a group of moving pedestrians by calculating a mathematical similarity between the computed features of current pixel content and the model of the single walking pedestrian, and identifying an individual pedestrian where a matching confidence is high, and analyzing a next portion of the pedestrian zone in the field of view where a matching confidence is low.

11. The method of claim 10, further comprising incrementing a pedestrian count where a high matching confidence indicates that an individual pedestrian has been identified.

12. The method of claim 11, further comprising generating an output representing the pedestrian count to an external device or location for storage or use.

13. The method of claim 10, further comprising providing the pedestrian count to a traffic management tool, wherein the traffic management tool is configured to enable a user to select a size and location of one or more of the field of view, the vehicle detection zone, and the stop bar.

14. The method of claim 10, wherein the identifying part-based features defining an individual pedestrian further comprises one or more of determining a width and a height of one or more object parts, comparing body structure combinations with one or more predetermined templates, and applying one or more geometric constraints to separate the part-based features.

15. The method of claim 10, further comprising applying a vehicle detection status to adjust sensitivity for detecting the one or more pedestrians in the pedestrian zone, the vehicle detection status including one or more of vehicular speed and traffic detection zone saturation.

16. The method of claim 15, further comprising dynamically changing a sensitivity for detecting the one or more pedestrians in the pedestrian zone by one or both of increasing a likelihood of a pedestrian crossing when a stopped vehicle is detected or no vehicle in present, and decreasing a likelihood of a pedestrian crossing while the vehicle traffic is free flowing.

17. The method of claim 10, wherein the field of view is captured by one or more sensors in or near a traffic intersection, the one or more sensors including at least one of video cameras, a radar system, and a magnetometer.

18. The method of claim 10, further comprising identifying one or more pedestrian characteristics in the traffic detection zone, that at least include a pedestrian presence, a pedestrian volume, a pedestrian velocity, and a pedestrian trajectory.

19. A system, comprising:

a computing environment including at least one non-transitory computer-readable storage medium having program instructions stored therein and a computer processor operable to execute the program instructions within one or more data processing modules configured to a) identify a region in a field of view of a traffic detection zone used by one or more pedestrians, and b) accurately count the one or more pedestrians in the traffic detection zone, the one or more data processing modules including;

a pedestrian count zone identification module configured to determine a pedestrian zone in the field of view, by a) identifying a position of at least one vehicle detection zone in nearest proximity to a stop bar, each at least one vehicle detection zone having a height that extends to or near to the stop bar, and b) calculating a height of a pedestrian area in the field of view from the height of the at least one vehicle detection zone;

a pedestrian detector learning module configured to count the one or more pedestrians in the pedestrian zone by, a) analyzing portions of the pedestrian zone in the field of view, b) computing features of current pixel content identified in the field of view by identifying part-based features defining an individual pedestrian that include one or more of body structure combinations, body shape, body width or walking gestures, c) developing a model of a single walking pedestrian to separate each individual pedestrian in a group of moving pedestrians in the field of view, by computing pedestrian features using pixels defining a pedestrian contour, d) determining a matching confidence between an individual pedestrian and a group of moving pedestrians by calculating a mathematical similarity between the computed features of current pixel content and the model of the single walking pedestrian, and e) incrementing a pedestrian count where a high matching confidence indicates that an individual pedestrian has been identified; and

an output module configured to communicate the pedestrian count to an external device or location.

20. The system of claim 19, wherein the pedestrian detector learning module is further configured to identify an individual pedestrian where a matching confidence is high, and analyzing a next portion of the pedestrian zone in the field of view where a matching confidence is low.

21. The system of claim 19, wherein the pedestrian detector learning module is further configured to count the one or more pedestrians in the pedestrian zone by one or more of determining a width and a height of one or more object parts, comparing body structure combinations with one or more predetermined templates, and applying one or more geometric constraints to separate the part-based features.

22. The system of claim 19, wherein the pedestrian count zone identification module is further configured to determine a pedestrian zone in the field of view by extending a length of the pedestrian area to a leftmost edge of the field of view, and a rightmost edge of the field of view, and by extending the height of the pedestrian area into a portion of the at least one vehicle detection zone.

23. The system of claim 19, wherein the pedestrian detector learning module is further configured to adjust sensitivity for detecting the one or more pedestrians in the pedestrian zone by applying a vehicle detection status that includes one or more of vehicular speed and traffic detection zone saturation.

24. The system of claim 23, wherein the pedestrian detector learning module is further configured to dynamically change a sensitivity for detecting the one or more pedestrians in the pedestrian zone by one or both of increasing a likelihood of a pedestrian crossing when a stopped vehicle is detected or no vehicle in present, and decreasing a likelihood of a pedestrian crossing while the vehicle traffic is free flowing.

25. The system of claim 19, wherein the field of view is captured by one or more sensors in or near a traffic intersection, the one or more sensors including at least one of video cameras, a radar system, and a magnetometer.

26. The system of claim 19, wherein the pedestrian detector learning module is further configured to identify one or more pedestrian characteristics in the traffic detection zone, that at least include a pedestrian presence, a pedestrian volume, a pedestrian velocity, and a pedestrian trajectory.

27. The system of claim 19, wherein the pedestrian count is provided to a traffic management tool configured to enable a user to select a size and location of one or more of the field of view, the vehicle detection zone, and the stop bar.