Higher-order spectral analysis of human motion

We describe a higher-order spectral analysis-based approach for detecting people by recognizing human motion such as walking or running. The periodic attribute of human motion lends itself to efficient spectral inspection. In the proposed method, the stride length is determined in every frame as the image sequence evolves. The bispectrum which is the Fourier transform of the triple correlation is a robust indicator of presence of periodicity. Triple correlation is robust as it is immune to any symmetrically distributed noise. The method is successfully tested on real video sequences