METHODS AND APPARATUS FOR SCALE RECOVERY FROM MONOCULAR VIDEO

1 .- 25 . (canceled)

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

segment an input image from a monocular video to detect an object in a camera field;

estimate camera parameters from the segmented input image;

iteratively refine the estimated camera parameters using object heights;

calculate a scale for the video;

iteratively refine the scale based on a user input; and

report scaling results for visualization.

27 . The non-transitory computer readable medium of claim 1 , wherein the input image segmentation is performed using a segmentation backbone network.

28 . The non-transitory computer readable medium of claim 1 , wherein the video scale is calculated using a first and second camera parameter.

29 . The non-transitory computer readable medium of claim 3 , wherein the first and second camera parameters are adjusted according to a projection model.

30 . The non-transitory computer readable medium of claim 1 , wherein the scaling results are reported via a graphical user interface.

31 . The non-transitory computer readable medium of claim 1 , wherein the object heights are used to train a branch of a neural network model.

32 . The non-transitory computer readable medium of claim 6 , wherein the branch of the neural network model is trained to adjust at least one of a first camera parameter or a second camera parameter.

33 . The non-transitory computer readable medium of claim 1 , wherein the user input for iterative scale refinement is provided via a graphical user interface.

34 . An apparatus to recover scale from monocular video comprising:

interface circuitry;

machine readable instructions; and

programmable circuitry to at least one of instantiate or execute the machine-readable instructions to:

segment an input image from the monocular video to detect an object in a camera field;

estimate camera parameters from the segmented input image; and

iteratively refine the estimated camera parameters using object heights;

calculate a scale for the video;

iteratively refine the scale based on a user input; and

report scaling results for visualization.

35 . The apparatus of claim 9 , wherein the input image segmentation is performed using a segmentation backbone network.

36 . The apparatus of claim 9 , wherein the video scale is calculated using a first and second camera parameter.

37 . The apparatus of claim 11 , wherein the first and second camera parameters are adjusted according to a projection model.

38 . The apparatus of claim 9 , wherein the scaling results are reported via a graphical user interface.

39 . The apparatus of claim 9 , wherein the object heights are obtained from a dataset.

40 . The apparatus of claim 14 , wherein the object heights are used to train a branch of a neural network model.

41 . The apparatus of claim 15 , wherein the branch of the neural network model is trained to adjust at least one of a first camera parameter or a second camera parameter.

42 . A method for scale recovery from monocular video, the method comprising:

segmenting an input image from the monocular video to detect an object in a camera field;

estimating camera parameters from the monocular input video;

iteratively refining the estimated camera parameters;

calculating a scale for relative depth;

iteratively refining the scale with provided user input; and

reporting scaling results for visualization.

43 . The method of claim 17 , wherein the input image segmentation is performed using a segmentation backbone network.

44 . The method of claim 17 , wherein the video scale is calculated using a first and second camera parameter.

45 . The method of claim 19 , wherein the first and second camera parameters are adjusted according to a projection model.