Authors:
Athira Nambiar
;
Alexandre Bernardino
;
Jacinto C. Nascimento
and
Ana Fred
Affiliation:
Instituto Superior Técnico, Portugal
Keyword(s):
Person Re-identification, Biometrics, Anthropometrics, Gait, Kinect, Data Fusion.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Motion, Tracking and Stereo Vision
;
Shape Representation and Matching
;
Video Surveillance and Event Detection
Abstract:
In this work, we present view-point invariant person re-identification (Re-ID) by multi-modal feature fusion of 3D soft biometric cues. We exploit the MS Kinect sensor v.2, to collect the skeleton points from the walking subjects and leverage both the anthropometric features and the gait features associated with the person. The key proposals of the paper are two fold: First, we conduct an extensive study of the influence of various features both individually and jointly (by fusion technique), on the person Re-ID. Second, we present an actual demonstration of the view-point invariant Re-ID paradigm, by analysing the subject data collected in different walking directions. Focusing the latter, we further analyse three different categories which we term as pseudo, quasi and full view-point invariant scenarios, and evaluate our system performance under these various scenarios. Initial pilot studies were conducted on a new set of 20 people, collected at the host laboratory. We illustrate,
for the first time, gait-based person re-identification with truly view-point invariant behaviour, i.e. the walking direction of the probe sample being not represented in the gallery samples.
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