Methods and apparatus to generate masked images based on selective privacy and/or location tracking

1. A stationary tracker comprising:

memory to store fixed geographic location information indicative of a fixed geographic location of the stationary tracker, and to store a reference feature image, the reference feature image received from a cloud service; and

at least one processor to:

determine a feature in an image is a non-displayable feature by comparing the feature to the reference feature image; and

generate a masked image, the masked image to mask the non-displayable feature based on the non-displayable feature not allowed to be displayed when captured from the fixed geographic location of the stationary tracker, and the masked image to display a displayable feature in the image.

2. The stationary tracker of claim 1 , further including a first convolutional neural network to detect the non-displayable feature in the image.

3. The stationary tracker of claim 2 , further including a second convolutional neural network to detect the displayable feature in the image.

4. The stationary tracker of claim 1 , further including a camera to capture the image.

5. The stationary tracker of claim 1 , wherein the feature in the image is at least one of: (a) a face of a person, or (b) a vehicle.

6. The stationary tracker of claim 1 , further including a wireless interface to detect a wireless tag located on a tag bearer, the at least one processor to determine the tag bearer is the displayable feature in the image based on the wireless tag.

7. The stationary tracker of claim 1 , further including a network interface to be in communication with the cloud service.

8. The stationary tracker of claim 7 , wherein the at least one processor is to determine the feature in the image is the non-displayable feature based on an instruction from the cloud service.

9. A stationary tracker comprising:

means for storing to:

store geographic location information indicative of a fixed geographic location of the stationary tracker; and

store a reference feature image;

means for processing to, based on information from a cloud service:

determine a feature in an image is a non-displayable feature based on: (a) a comparison of the feature to the reference feature image, and (b) the geographic location information, wherein the non-displayable feature is not allowed to be displayed when captured from the fixed geographic location of the stationary tracker; and

generate a masked image, the masked image to mask the non-displayable feature, and the masked image to display a displayable feature in the image; and

first means for generating a first probability value to detect the non-displayable feature in the image.

10. The stationary tracker of claim 9 , further including second means for generating a second probability value to detect the displayable feature in the image.

11. The stationary tracker of claim 9 , further including means for capturing the image.

12. The stationary tracker of claim 9 , wherein the feature in the image is at least one of: (a) a face of a person, or (b) a vehicle.

13. The stationary tracker of claim 9 , further including means for detecting a wireless tag located on a tag bearer, the means for processing to determine the tag bearer is the displayable feature in the image based on the wireless tag.

14. The stationary tracker of claim 9 , wherein the information from the cloud service includes at least one of an instruction or the reference feature image.

15. A stationary tracker comprising:

memory to store fixed geographic location information indicative of a fixed geographic location of the stationary tracker, and to store a reference feature image; and

at least one processor to, based on information from a cloud service:

recognize a non-displayable feature in an image by comparing the non-displayable feature to the reference feature image;

determine a tag bearer in the image is a displayable feature based on a wireless tag located on the tag bearer; and

generate a masked image, the masked image to display the displayable feature based on the displayable feature being allowed to be displayed when captured from the fixed geographic location of the stationary tracker, and the masked image to mask the non-displayable feature in the image.

16. The stationary tracker of claim 15 , further including a convolutional neural network to detect the non-displayable feature in the image before the at least one processor is to recognize the non-displayable feature.

17. The stationary tracker of claim 15 , further including a camera to capture the image.

18. The stationary tracker of claim 15 , wherein the non-displayable feature in the image is at least one of: (a) a face of a person, or (b) a vehicle.

19. The stationary tracker of claim 15 , wherein the information from the cloud service includes at least one of an instruction or a tag identifier.

20. A non-transitory computer readable storage medium comprising computer readable instructions that, when executed, cause at least one processor to at least:

access an image captured by an image capture device of a stationary tracker, the stationary tracker located at a fixed geographic location;

recognize a non-displayable feature in the image by comparing the non-displayable feature to a reference feature image;

access an identifier of a wireless tag located on a tag bearer;

determine the tag bearer is a displayable feature in the image based on the identifier of the wireless tag; and

generate a masked image, the masked image to mask the non-displayable feature based on the non-displayable feature not allowed to be displayed when captured from the fixed geographic location, and the masked image to display the displayable feature in the image.

21. The non-transitory computer readable storage medium of claim 20 , wherein the instructions are to cause the at least one processor to execute a first convolutional neural network to detect the non-displayable feature in the image.

22. The non-transitory computer readable storage medium of claim 21 , wherein the instructions are to cause the at least one processor to execute a second convolutional neural network to detect the displayable feature in the image.

23. The non-transitory computer readable storage medium of claim 20 , wherein the non-displayable feature in the image is at least one of: (a) a face of a person, or (b) a vehicle.

24. The non-transitory computer readable storage medium of claim 20 , wherein the image capture device is a camera.

25. The non-transitory computer readable storage medium of claim 20 , wherein the instructions are to cause the at least one processor to access the reference feature image in a database, the reference feature image from a cloud service.

26. A non-transitory computer readable storage medium comprising computer readable instructions that, when executed, cause at least one processor to at least:

access an image captured by an image capture device of a stationary tracker, the stationary tracker located at a fixed geographic location;

recognize a non-displayable feature in the image based on a cloud service by comparing the non-displayable feature to a reference feature image; and

generate a masked image based on the cloud service, the masked image to mask the non-displayable feature based on the non-displayable feature not allowed to be displayed when captured from the fixed geographic location, and the masked image to display a displayable feature in the image.

27. The non-transitory computer readable storage medium of claim 26 , wherein the instructions are to cause the at least one processor to execute a first convolutional neural network to detect the non-displayable feature in the image.

28. The non-transitory computer readable storage medium of claim 26 , wherein the instructions are to cause the at least one processor to execute a second convolutional neural network to detect the displayable feature in the image.

29. The non-transitory computer readable storage medium of claim 26 , wherein the non-displayable feature in the image is at least one of: (a) a face of a person, or (b) a vehicle.

30. The non-transitory computer readable storage medium of claim 26 , wherein the instructions are to cause the at least one processor to:

detect a wireless tag located on a tag bearer; and

determine the tag bearer is the displayable feature in the image based on the wireless tag.

31. The non-transitory computer readable storage medium of claim 26 , wherein the instructions are to cause the at least one processor to access the reference feature image in a database, the reference feature image from the cloud service.

32. A method comprising:

accessing an image captured by an image capture device of a stationary tracker, the stationary tracker located at a fixed geographic location;

recognizing a non-displayable feature in the image based on a cloud service by comparing the non-displayable feature to a reference feature image; and

generating a masked image based on the cloud service, the masked image to mask the non-displayable feature based on the non-displayable feature not allowed to be displayed when captured from the fixed geographic location, and the masked image to display a displayable feature in the image.

33. The method of claim 32 , further including executing a first convolutional neural network to detect the non-displayable feature in the image.

34. The method of claim 32 , further including executing a second convolutional neural network to detect the displayable feature in the image.

35. The method of claim 32 , wherein the non-displayable feature in the image is at least one of: (a) a face of a person, or (b) a vehicle.

36. The method of claim 32 , further including:

detecting a wireless tag located on a tag bearer; and

determining the tag bearer is the displayable feature in the image based on the wireless tag.

37. The method of claim 32 , further including accessing the reference feature image in a database, the reference feature image from the cloud service.