In order to analyse surveillance video, we need to efficiently explore large datasets containing videos of walking humans. At this resolution, the human walk (their gait) can be determined automatically more readily than other features, such as the face. Analysis can rely 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. We explore the content-based retrieval of videos containing walking subjects using semantic queries. We evaluate current biometric research using gait, unique in their 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 from the information retrieval community.