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In comparison to previous work, the proposed method utilizes hierarchical classification and treats driving behavior in terms of a spatio-temporal reference framework as opposed to a static image. The approach was verified using the Southeast University Driving-Posture Dataset, a dataset comprised of video clips covering aspects of driving such as: normal driving, responding to a cell phone call, eating and smoking. After pre-processing for illumination variations and motion sequence segmentation, eight classes of behavior were identified. The overall prediction accuracy obtained using the proposed approach was [Formula: see text] when using a hierarchical classification approach. The proposed approach was able to clearly identify two dangerous driving behaviors, Responding to a cellphone call and Eating, with recognition rates of 92.39% and 92.29% respectively. <\/jats:p>","DOI":"10.1142\/s0218001416500105","type":"journal-article","created":{"date-parts":[[2016,1,21]],"date-time":"2016-01-21T02:49:48Z","timestamp":1453344588000},"page":"1650010","source":"Crossref","is-referenced-by-count":29,"title":["Video-Based Classification of Driving Behavior Using a Hierarchical Classification System with Multiple Features"],"prefix":"10.1142","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3133-1836","authenticated-orcid":false,"given":"Chao","family":"Yan","sequence":"first","affiliation":[{"name":"Department of Computer Science and Software Engineering, Xian Jiaotong-Liverpool University, SIP, Suzhou 215123, P. R. 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