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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25 frames per second using 352×288 CIF images compressed using MPEG4 (http://www.info4security.com/story.asp?storyCode=3093501).
In practice several r values are attempted to choose an optimal number of concepts for a given dataset.
i.e. only semantic terms, visual terms set to 0
i.e. only visual terms, semantic terms set to 0
chosen to disregard scaling effects Papadimitriou et al. [41]
http://www.statistics.gov.uk/about/Classifications/ns_ethnic_classification.asp Ethnic classification.
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Samangooei, S., Nixon, M.S. Performing content-based retrieval of humans using gait biometrics. Multimed Tools Appl 49, 195–212 (2010). https://doi.org/10.1007/s11042-009-0391-8
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DOI: https://doi.org/10.1007/s11042-009-0391-8