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However, fast and reliable detection of abnormal events is still a challenging work. Here, we introduce a two\u2010stream approach that offers an autoencoder\u2010based structure for fast and efficient detection to facilitate anomaly detection from surveillance video without labeled abnormal events. Furthermore, we present post hoc interpretability of feature map visualization to show the process of feature learning, revealing uncertain and ambiguous decision boundaries in the video sequence. Experimental results on Avenue, UCSD Ped2, and Subway datasets show that our method can detect abnormal events well and explain the internal logic of the model at the object level.<\/jats:p>","DOI":"10.1155\/2021\/7367870","type":"journal-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T19:05:08Z","timestamp":1627412708000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Anomaly Detection in Videos Using Two\u2010Stream Autoencoder with Post Hoc Interpretability"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9155-1865","authenticated-orcid":false,"given":"Jiangfan","family":"Feng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yukun","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,7,27]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCDS.2018.2883368"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2572609"},{"key":"e_1_2_9_3_2","doi-asserted-by":"crossref","unstructured":"FeichtenhoferC. 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