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This results in datasets where anomalies are placed in a single category, and are often considered less relevant from a security standpoint. Instead, we propose to create video anomaly datasets based on a framework utilizing object annotations to ease the annotation process and allow users to decide on the anomaly definition. Furthermore, this allows for a fine-grained evaluation w.r.t.\u00a0anomaly types, which represents a novelty in the area of video anomaly detection. The framework is demonstrated using the existing thermal long-term drift (LTD) dataset, identifying and evaluating five different types of anomalies (appearance, motion, localization, density, and tampering) on six test sets. State-of-the-art anomaly detection methods are evaluated and found to underperform on the thermal anomaly detection dataset, which emphasizes a need for an adjustable anomaly definition in order to produce better anomaly datasets and models that generalize towards practical use. We share the code of the proposed framework to extract anomaly types along with object annotations for the LTD dataset at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/jagob\/harborfront-vad\" ext-link-type=\"uri\">https:\/\/github.com\/jagob\/harborfront-vad<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s11063-024-11696-9","type":"journal-article","created":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T05:16:12Z","timestamp":1738041372000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Harborfront Anomaly Detection"],"prefix":"10.1007","volume":"57","author":[{"given":"Jacob V.","family":"Dueholm","sequence":"first","affiliation":[]},{"given":"Mia","family":"Siemon","sequence":"additional","affiliation":[]},{"given":"Radu T.","family":"Ionescu","sequence":"additional","affiliation":[]},{"given":"Thomas B.","family":"Moeslund","sequence":"additional","affiliation":[]},{"given":"Kamal","family":"Nasrollahi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,28]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Acsintoae A, Florescu A, Georgescu M, et\u00a0al (2022) Ubnormal: new benchmark for supervised open-set video anomaly detection. 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