imosse

menpofit.math.imosse(A, B, n_ab, X, y, l=0.01, boundary='constant', crop_filter=True, f=1.0)[source]

Incremental Minimum Output Sum of Squared Errors (iMOSSE) filter.

Parameters

  • A ((N,) ndarray) – The current auto-correlation array, where N = (patch_h+response_h-1) * (patch_w+response_w-1) * n_channels.

  • B ((N, N) ndarray) – The current cross-correlation array, where N = (patch_h+response_h-1) * (patch_w+response_w-1) * n_channels.

  • n_ab (int) – The current number of images.

  • X ((n_images, n_channels, image_h, image_w) ndarray) – The training images (patches).

  • y ((1, response_h, response_w) ndarray) – The desired response.

  • l (float, optional) – Regularization parameter.

  • boundary ({'constant', 'symmetric'}, optional) – Determines how the image is padded.

  • crop_filter (bool, optional) – If True, the shape of the MOSSE filter is the same as the shape of the desired response. If False, the filter’s shape is equal to: X[0].shape + y.shape - 1

  • f ([0, 1] float, optional) – Forgetting factor that weights the relative contribution of new samples vs old samples. If 1.0, all samples are weighted equally. If <1.0, more emphasis is put on the new samples.

Returns

  • f ((1, response_h, response_w) ndarray) – Minimum Output Sum od Squared Errors (MOSSE) filter associated to the training images.

  • sXY ((N,) ndarray) – The auto-correlation array, where N = (image_h+response_h-1) * (image_w+response_w-1) * n_channels.

  • sXX ((N, N) ndarray) – The cross-correlation array, where N = (image_h+response_h-1) * (image_w+response_w-1) * n_channels.

References

1

D. S. Bolme, J. R. Beveridge, B. A. Draper, and Y. M. Lui. “Visual Object Tracking using Adaptive Correlation Filters”, IEEE Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR), 2010.