Performing content-based retrieval of humans using gait biometrics |
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Authors: | Sina Samangooei Mark S Nixon |
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Affiliation: | (1) School of Electronics and Computer Science, Southampton University, Southampton, SO17 1BJ, UK |
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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). |
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