System and method for detecting, tracking, and classifiying objects

1. A method for discerning a vehicle at an access control point, the method comprising:

obtaining a video sequence of the access control point from a first camera operable to capture video of the vehicle approaching a first outer gate;

detecting an object of interest from the video sequence;

tracking the object from the video sequence to obtain tracked-object data;

classifying the object to obtain classified-object data;

determining that the object is the vehicle based on the classified-object data;

determining if the vehicle is being tailgated based on the classified-object data;

determining that the vehicle is present in a predetermined detection zone of the access control point based on the tracked-object data; and

controlling activation of the first outer gate and a second inner gate at the access control point based on the determining if the vehicle is being tailgated.

2. The method of claim 1 , wherein detecting the object of interest further comprises:

employing a stochastic background modeling technique or a segmentation technique to detect the object in the video sequence.

3. The method of claim 1 , wherein tracking the object comprises:

employing a motion detection technique to track the object in the video sequence.

4. The method of claim 1 , wherein the detecting the object of interest further comprises:

extracting background components from the video sequence.

5. The method of claim 1 , wherein tracking the object comprises:

determining if a pixel in a frame of the video sequence represents an object that is moving based on a stochastic model of a background scene in the frame; and

clustering pixels in the frame that represent the object that is moving.

6. The method of claim 5 , wherein tracking the object comprises:

identifying a position of the object in the frame of the video sequence;

identifying candidate objects in a next frame of the video sequence; and

comparing the object in the frame with candidate objects in the next frame to determine a next position of the object in the next frame.

7. The method of claim 1 , wherein classifying the object comprises:

comparing the object with one or more vehicle classifications to determine a type of vehicle that is similar to the tracked object using a 3-D model based fitting technique.

8. The method of claim 7 , wherein the one or more vehicle classifications are based on computer-assisted drawings of vehicle types.

9. The method of claim 7 , wherein comparing the object comprises:

comparing the object based on fitting the object to a model of a vehicle having between 10-30 vertices and between 10-40 facets to approximate a mean shape of a computer-assisted drawing of the vehicle.

10. A device for discerning a vehicle at an access control point, the device comprising:

a memory containing instructions; and

at least one processor, operably connected to the memory, that executes the instructions to perform operations comprising:

obtaining a video sequence of the access control point from a first camera operable to capture video of the vehicle approaching a first outer gate;

detecting an object of interest from the video sequence;

tracking the object from the video sequence to obtain tracked-object data;

classifying the object to obtain classified-object data;

determining that the object is the vehicle based on the classified-object data;

determining if the vehicle is being tailgated based on the classified-object data;

determining that the vehicle is present in a predetermined detection zone of the access control point based on the tracked-object data; and

controlling activation of the first outer gate and a second inner gate at the access control point based on the determining if the vehicle is being tailgated.

11. The device of claim 10 , wherein detecting the object of interest further comprises:

employing a stochastic background modeling technique or a segmentation technique to detect the object in the video sequence.

12. The device of claim 10 , wherein tracking the object comprises:

employing a motion detection technique to track the object in the video sequence.

13. The device of claim 10 , wherein the detecting the object of interest further comprises:

extracting background components from the video sequence.

14. The device of claim 10 , wherein tracking the object comprises:

determining if a pixel in a frame of the video sequence represents an object that is moving based on a stochastic model of a background scene in the frame; and

clustering pixels in the frame that represent the object that is moving.

15. The device of claim 14 , wherein tracking the object comprises:

identifying a position of the object in the frame of the video sequence;

identifying candidate objects in a next frame of the video sequence; and

comparing the object in the frame with candidate objects in the next frame to determine a next position of the object in the next frame.

16. The device of claim 10 , wherein classifying the object comprises:

comparing the object with one or more vehicle classifications to determine a type of vehicle that is similar to the tracked object using a 3-D model based fitting technique.

17. The device of claim 16 , wherein the one or more vehicle classifications are based on computer-assisted drawings of vehicle types.

18. The device of claim 16 , wherein comparing the object comprises:

comparing the object based on fitting the object to a model of a vehicle having between 10-30 vertices and between 10-40 facets to approximate a mean shape of a computer-assisted drawing of the vehicle.

19. A non-transitory computer readable storage medium comprising instructions for causing one or more processors to perform a method for discerning a vehicle at an access control point, the method comprising:

obtaining a video sequence of the access control point from a first camera operable to capture video of the vehicle approaching a first outer gate;

detecting an object of interest from the video sequence;

tracking the object from the video sequence to obtain tracked-object data;

classifying the object to obtain classified-object data;

determining that the object is the vehicle based on the classified-object data;

determining if the vehicle is being tailgated based on the classified-object data;

determining that the vehicle is present in a predetermined detection zone of the access control point based on the tracked-object data; and

controlling activation of the first outer gate and a second inner gate at the access control point based on the determining if the vehicle is being tailgated.

20. The computer readable storage medium of claim 19 , wherein detecting the object of interest further comprises:

employing a stochastic background modeling technique or a segmentation technique to detect the object in the video sequence.

21. The computer readable storage medium of claim 19 , wherein tracking the object comprises:

employing a motion detection technique to track the object in the video sequence.

22. The computer readable storage medium of claim 19 , wherein the detecting the object of interest further comprises:

extracting background components from the video sequence.

23. The computer readable storage medium of claim 19 , wherein tracking the object comprises:

determining if a pixel in a frame of the video sequence represents an object that is moving based on a stochastic model of a background scene in the frame; and

clustering pixels in the frame that represent the object that is moving.

24. The computer readable storage medium of claim 19 , wherein tracking the object comprises:

identifying a position of the object in the frame of the video sequence;

identifying candidate objects in a next frame of the video sequence; and

comparing the object in the frame with candidate objects in the next frame to determine a next position of the object in the next frame.

25. The computer readable storage medium of claim 19 , wherein classifying the object comprises:

comparing the object with one or more vehicle classifications to determine a type of vehicle that is similar to the tracked object using a 3-D model based fitting technique.

26. The computer readable storage medium of claim 25 , wherein the one or more vehicle classifications are based on computer-assisted drawings of vehicle types.

27. The computer readable storage medium of claim 25 , wherein comparing the object comprises:

comparing the object based on fitting the object to a model of a vehicle having between 10-30 vertices and between 10-40 facets to approximate a mean shape of a computer-assisted drawing of the vehicle.