Human Gait Recognition from Motion Capture Data in Signature Poses

Most contribution to the field of structure-based human gait recognition has been done through design of extraordinary gait features. Many research groups that address this topic introduce a unique combination of gait features, select a couple of well-known object classiers, and test some variations of their methods on their custom Kinect databases. For a practical system, it is not necessary to invent an ideal gait feature -- there have been many good geometric features designed -- but to smartly process the data there are at our disposal. This work proposes a gait recognition method without design of novel gait features; instead, we suggest an effective and highly efficient way of processing known types of features. Our method extracts a couple of joint angles from two signature poses within a gait cycle to form a gait pattern descriptor, and classifies the query subject by the baseline 1-NN classier. Not only are these poses distinctive enough, they also rarely accommodate motion irregularities that would result in confusion of identities. We experimentally demonstrate that our gait recognition method outperforms other relevant methods in terms of recognition rate and computational complexity. Evaluations were performed on an experimental database that precisely simulates street-level video surveillance environment

An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods

As a contribution to reproducible research, this paper presents a framework and a database to improve the development, evaluation and comparison of methods for gait recognition from Motion Capture (MoCap) data. The evaluation framework provides implementation details and source codes of state-of-the-art human-interpretable geometric features as well as our own approaches where gait features are learned by a modification of Fisher's Linear Discriminant Analysis with the Maximum Margin Criterion, and by a combination of Principal Component Analysis and Linear Discriminant Analysis. It includes a description and source codes of a mechanism for evaluating four class separability coefficients of feature space and four rank-based classifier performance metrics. This framework also contains a tool for learning a custom classifier and for classifying a custom query on a custom gallery. We provide an experimental database along with source codes for its extraction from the general CMU MoCap database

Absolute emission altitude of pulsars: PSRs B1839+09, B1916+14 and B2111+46

We study the mean profiles of the multi--component pulsars PSRs B1839+09, B1916+14 and B2111+46. We estimate the emission height of the core components, and hence find the absolute emission altitudes corresponding to the conal components. By fitting Gaussians to the emission components, we determine the phase location of the component peaks. Our findings indicate that the emission beams of these pulsars have the nested core--cone structures. Based on the phase location of the component peaks, we estimate the aberration--retardation (A/R) phase shifts in the profiles. Due to the A/R phase shift, the peak of the core component in the intensity profile and the inflection point of the polarization angle swing are found to be symmetrically shifted in the opposite directions with respect to the meridional plane in such a way that the core shifts towards the leading side and the polarization angle inflection point towards the trailing side. We have been able to locate the phase location of the meridional plane and to estimate the absolute emission altitude of both the core and the conal components relative to the neutron star centre, using the exact expression for the A/R phase shift given by Gangadhara (2005).Comment: 10 pages, 6 figures, Accepted for Publication in A&

Intelligent Sensors for Human Motion Analysis

Currently, the analysis of human motion is one of the most interesting and active research topics in computer science, especially in computer vision [...