Gait Analysis using Independent Components of image motion

We propose a novel approach to gait analysis using the independent components of motion. Our motion representation uses the amount of translation in small image patches. For each subject in the training set, several short image sequences are selected at random. Spatiotemporal independent component analysis (stICA) estimates independent components (ICs) of the training sequences. Given an unknown subject, we compute stIC coefficients for all image subsequences. These coefficients are compared with the training set using the cosine similarity measure. We demonstrate feasibility by using the nearest neighbor to classify the gender of a subject.