{"id":{"repo_id":"essex","oai_identifier":"oai:repository.essex.ac.uk:26224"},"canonical_url":"https://search.dev.ndltd.org/etd/essex/oai:repository.essex.ac.uk:26224","repository":{"repo_id":"essex","name":"University of Essex","base_url":"https://repository.essex.ac.uk/cgi/oai2"},"display":{"title":"Video Object Counting in Unconstrained Environments Using Density-Based Clustering","abstract":"In this thesis, we present a video object counting approach using multiple local feature matching. We explain the development of a dataset with which to test our approach. Our dataset uses a new approach which we designed to extract object ground truth. We also provide a comparison of common single object trackers. We develop a multi-object tracker named Learn-Select-Track and use it to track the colours of objects of interest to filter out false positive object localisations. 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