Performing content-based retrieval of humans using gait biometrics

Samangooei, S. and Nixon, M. (2010) Performing content-based retrieval of humans using gait biometrics. Multimed Tools Applications, 49 (1). pp. 195-212.

Download

Preview

PDF
708Kb

Abstract

In order to analyse surveillance video, we need to efficiently explore large
datasets containing videos of walking humans. Effective analysis of such data relies
on retrieval of video data which has been enriched using semantic annotations. A
manual annotation process is time-consuming and prone to error due to subject
bias however, at surveillance-image resolution, the human walk (their gait) can
be analysed automatically. We explore the content-based retrieval of videos containing
walking subjects, using semantic queries. We evaluate current research in
gait biometrics, unique in its effectiveness at recognising people at a distance. We
introduce a set of semantic traits discernible by humans at a distance, outlining
their psychological validity. Working under the premise that similarity of the chosen
gait signature implies similarity of certain semantic traits we perform a set of
semantic retrieval experiments using popular Latent Semantic Analysis techniques.
We perform experiments on a dataset of 2000 videos of people walking in laboratory
conditions and achieve promising retrieval results for features such as Sex (mAP= 14% above random), Age (mAP=10% above random) and Ethnicity (mAP=9% above random)

Item Type:

Article

Creator/Authors: