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The existing algorithms for detection require accurate modeling of the background, and most of them use the characteristics of two\u2010dimensional images such as area to distinguish the type of the target. However, these algorithms significantly depend on the background and are lack of accuracy on the type of distinction. Therefore, this paper proposes an algorithm for detecting parking and dropping objects that uses real three\u2010dimensional information to distinguish the type of target. Firstly, an abnormal region is initially defined based on status change, when there is an object that did not exist before in the traffic scene. Secondly, the preliminary determination of the abnormal area is bidirectionally tracked to determine the area of parking and dropping objects, and the eight\u2010neighbor seed filling algorithm is used to segment the parking and the dropping object area. Finally, a three\u2010view recognition method based on inverse projection is proposed to distinguish the parking and dropping objects. The method is based on the matching of the three\u2010dimensional structure of the vehicle body. In addition, the three\u2010dimensional wireframe of the vehicle extracted by the back\u2010projection can be used to match the structural model of the vehicle, and the vehicle model can be further identified. The 3D wireframe of the established vehicle is efficient and can meet the needs of real\u2010time applications. And, based on experimental data collected in tunnels, highways, urban expressways, and rural roads, the proposed algorithm is verified. The results show that the algorithm can effectively detect the parking and dropping objects within different environment, with low miss and false detection rate.<\/jats:p>","DOI":"10.1155\/2019\/2950287","type":"journal-article","created":{"date-parts":[[2019,4,7]],"date-time":"2019-04-07T23:30:51Z","timestamp":1554679851000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Effective Algorithm for Video\u2010Based Parking and Drop Event Detection"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7436-1595","authenticated-orcid":false,"given":"Gang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8590-0061","authenticated-orcid":false,"given":"Huansheng","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,4,7]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/1485652"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2542804"},{"key":"e_1_2_9_3_2","doi-asserted-by":"crossref","unstructured":"BevilacquaA.andVaccariS. 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