{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T14:48:47Z","timestamp":1784818127073,"version":"3.55.0"},"reference-count":74,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T00:00:00Z","timestamp":1636416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002753","name":"National University of Colombia","doi-asserted-by":"publisher","award":["BEDA2019I"],"award-info":[{"award-number":["BEDA2019I"]}],"id":[{"id":"10.13039\/501100002753","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>According to worldwide statistics, traffic accidents are the cause of a high percentage of violent deaths. The time taken to send the medical response to the accident site is largely affected by the human factor and correlates with survival probability. Due to this and the wide use of video surveillance and intelligent traffic systems, an automated traffic accident detection approach becomes desirable for computer vision researchers. Nowadays, Deep Learning (DL)-based approaches have shown high performance in computer vision tasks that involve a complex features relationship. Therefore, this work develops an automated DL-based method capable of detecting traffic accidents on video. The proposed method assumes that traffic accident events are described by visual features occurring through a temporal way. Therefore, a visual features extraction phase, followed by a temporary pattern identification, compose the model architecture. The visual and temporal features are learned in the training phase through convolution and recurrent layers using built-from-scratch and public datasets. An accuracy of 98% is achieved in the detection of accidents in public traffic accident datasets, showing a high capacity in detection independent of the road structure.<\/jats:p>","DOI":"10.3390\/computers10110148","type":"journal-article","created":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T12:46:23Z","timestamp":1636461983000},"page":"148","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Automatic Detection of Traffic Accidents from Video Using Deep Learning Techniques"],"prefix":"10.3390","volume":"10","author":[{"given":"Sergio","family":"Robles-Serrano","sequence":"first","affiliation":[{"name":"Facultad de Minas, Universidad Nacional de Colombia, Sede Medell\u00edn 050041, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9069-0732","authenticated-orcid":false,"given":"German","family":"Sanchez-Torres","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad del Magdalena, Santa Marta 470001, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0378-028X","authenticated-orcid":false,"given":"John","family":"Branch-Bedoya","sequence":"additional","affiliation":[{"name":"Facultad de Minas, Universidad Nacional de Colombia, Sede Medell\u00edn 050041, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s10255-004-0150-6","article-title":"The Road Traffic Analysis Based on an Urban Traffic Model of the Circular Working Field","volume":"20","author":"Li","year":"2004","journal-title":"Acta Math. 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