Histograms of optical flow for efficient representation of body motion

A novel method for efficient encoding of human body motion, extracted from image sequences is presented. Optical flow field is calculated from sequential images, and the part of the flow field containing a person is subdivided into six segments. For each of the segments, a two dimensional, eight-bin histogram of optical flow is calculated. A symbol is generated, corresponding to the bin with the maximum sample count. Since the optical flow sequences before and after the temporal reference point are processed separately, twelve symbol sequences are obtained from the whole image sequence. Symbol sequences are purged of all symbol repetitions. To establish the similarity between two motion sequences, two sets of symbol sequences are compared. In our case, this is done by the means of normalized Levenshtein distance. Due to use of symbol sequences, the method is extremely storage efficient. It is also performance efficient, as it could be performed in near-realtime using the motion vectors from MPEG4 encoded video sequences. The approach has been tested on video sequences of persons entering restricted area using keycard and fingerprint reader. We show that it could be applied both to verification of person identities due to minuscule differences in their motion, and to detection of unusual behavior, such as tailgating.