Differences to Dalal & Triggs Formulation

When using HOGpp, one should be aware of subtle differences between the integral histogram implementation and the one originally proposed by Dalal & Triggs.

In general, computing R-HOG consists of the following steps:

  1. (optional) gamma correction

  2. gradient computation

  3. orientation binning within a cell

    • down-weighting of pixels using a Gaussian with respect to their position within a block

    • trilinear interpolation of magnitude votes between neighboring bins in both orientation and position

  4. block normalization

Provided these steps, R-HOG extracted using an integral histogram is slightly inferior to the original formulation. The reason for this being that neither pixel down-weighting using a Gaussian nor trilinear interpolation can be performed efficiently within the integral histogram framework. However, the integral histogram R-HOG formulation is substantially faster while being a sufficiently close approximation to the original R-HOG formulation.

Despite the above limitations, our evaluation on the INRIA person dataset and the comparison against OpenCV’s HOGDescriptor indicates that particularly the Gaussian down-weighting does not necessarily improve the generalization ability of the associated classifiers.

For a comparison of both approaches, the interested reader should refer to Zhu et al. [ZYCA06]. Additional evaluation of related approaches can be found in [DTPB09].