{"title":"基于三维直方图和时间模式选择的运动分割","authors":"D. Mukherjee, Q. M. J. Wu","doi":"10.1109/ICMEW.2012.90","DOIUrl":null,"url":null,"abstract":"Motion segmentation has been a well explored research topic due to its vast application area. This work proposes a real-time motion segmentation method based on 3D histogram and temporal mode selection. The temporal distribution of a video sequence consists of the motion in the foreground and the relatively immobile background. A 3D histogram provides a short-term memory of the aforementioned distribution. The temporal mode selection process involves identifying the most frequent values in the distribution and construct the background thereafter. This work provides a detailed analysis of the proposed method along with an easy-to-implement algorithm. A number of experimental results and comparisons with some of the leading algorithms are provided to show that the proposed method can provide real-time, robust and highly accurate results.","PeriodicalId":385797,"journal":{"name":"2012 IEEE International Conference on Multimedia and Expo Workshops","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Motion Segmentation Based on 3D Histogram and Temporal Mode Selection\",\"authors\":\"D. Mukherjee, Q. M. J. Wu\",\"doi\":\"10.1109/ICMEW.2012.90\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Motion segmentation has been a well explored research topic due to its vast application area. This work proposes a real-time motion segmentation method based on 3D histogram and temporal mode selection. The temporal distribution of a video sequence consists of the motion in the foreground and the relatively immobile background. A 3D histogram provides a short-term memory of the aforementioned distribution. The temporal mode selection process involves identifying the most frequent values in the distribution and construct the background thereafter. This work provides a detailed analysis of the proposed method along with an easy-to-implement algorithm. A number of experimental results and comparisons with some of the leading algorithms are provided to show that the proposed method can provide real-time, robust and highly accurate results.\",\"PeriodicalId\":385797,\"journal\":{\"name\":\"2012 IEEE International Conference on Multimedia and Expo Workshops\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Multimedia and Expo Workshops\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMEW.2012.90\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Multimedia and Expo Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMEW.2012.90","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Motion Segmentation Based on 3D Histogram and Temporal Mode Selection
Motion segmentation has been a well explored research topic due to its vast application area. This work proposes a real-time motion segmentation method based on 3D histogram and temporal mode selection. The temporal distribution of a video sequence consists of the motion in the foreground and the relatively immobile background. A 3D histogram provides a short-term memory of the aforementioned distribution. The temporal mode selection process involves identifying the most frequent values in the distribution and construct the background thereafter. This work provides a detailed analysis of the proposed method along with an easy-to-implement algorithm. A number of experimental results and comparisons with some of the leading algorithms are provided to show that the proposed method can provide real-time, robust and highly accurate results.