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Both the texture and color local features are extracted from the incoming frames independently and they are combined at the classification level to improve the object detection results. Here, each incoming image frames are subdivided into several regions and the median-based scale invariant local ternary pattern (MD-SILTP) is obtained for each sub-region. Based on the MD-SILTP patterns, the texture histograms are computed and matched with the background model using the histogram intersection method. Furthermore, the color features are extracted through color histogram matching technique. The background model is then updated based on the best matching texture and color histograms. Finally, the color and texture information are combined for final feature classification. Experiment results illustrate that the fusion of MD-SILTP texture with the color features is stable than the others under smooth surface regions, image noises due to illumination changes, moving cast shadow, and scaling problems.<\/jats:p>","DOI":"10.3233\/jifs-162231","type":"journal-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T11:43:40Z","timestamp":1501242220000},"page":"1933-1943","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":9,"title":["Moving object detection using median-based scale invariant local ternary pattern for video surveillance system"],"prefix":"10.1177","volume":"33","author":[{"given":"K.","family":"Kalirajan","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, SVS College of Engineering, Coimbatore, India"}]},{"given":"M.","family":"Sudha","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Hindusthan College of Engineering and Technology, Coimbatore, India"}]}],"member":"179","published-online":{"date-parts":[[2017,7,27]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2005.11.010"},{"key":"e_1_3_1_3_2","first-page":"154","author":"Zhang Z.","unstructured":"ZhangZ., WangC., XiaoB., LiuS., ZhouW., Multi-scale Fusion of Texture and Color for Background Modeling, In: 2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp. 154\u2013159. doi: 10.1109\/AVSS.2012.48","journal-title":"2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance (AVSS)"},{"key":"e_1_3_1_4_2","first-page":"1151","article-title":"Background and foreground modeling using nonparametric kernel density estimation for visual surveillance","volume":"90","author":"Elgammal A.","unstructured":"ElgammalA., DuraiswamiR., HarwoodD., DavisL., Background and foreground modeling using nonparametric kernel density estimation for visual surveillance, In: 2002 Proceeding IEEE, 90, pp. 1151\u20131163. doi: 10.1109\/JPROC.2002.801448","journal-title":"2002 Proceeding IEEE"},{"key":"e_1_3_1_5_2","first-page":"2","article-title":"Adaptive background mixture models for real-time tracking","author":"Stauffer C.","year":"1999","unstructured":"StaufferC. and GrimsonW.E.L., Adaptive background mixture models for real-time tracking, In Proc IEEE Computer Soc Conf Computer Vis Pattern Recognition, 1999, 2. doi: 10.1109\/CVPR.1999.784637","journal-title":"Proc IEEE Computer Soc Conf Computer Vis Pattern Recognition"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2002.1017623"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.68"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-75690-3_13"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2008.08.014"},{"key":"e_1_3_1_10_2","first-page":"1301","article-title":"Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes","author":"Liao S.","year":"2010","unstructured":"LiaoS., ZhaoG., KellokumpuV., PietikainenM., LiS.Z., Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. 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