2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR)最新文献

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Measuring Crowd Collectiveness with Trajectory Smoothing 基于轨迹平滑的群体集体性测度
Q. Phu, Vy Nguyen, Tien Do, T. Ngo
{"title":"Measuring Crowd Collectiveness with Trajectory Smoothing","authors":"Q. Phu, Vy Nguyen, Tien Do, T. Ngo","doi":"10.1109/MAPR.2018.8337520","DOIUrl":"https://doi.org/10.1109/MAPR.2018.8337520","url":null,"abstract":"Analyzing videos of crowd scenes plays an important role in surveillance systems. It is essential to measure crowd collectiveness, which indicates the degree of individuals acting as a union. State-of-the-art approaches mainly relied on the similarity of individual behaviors inside and outside neighborhood to quantify the topological structures of collective manifolds of crowd. Individual behaviors were estimated using velocity of individual trajectories tracked in consecutive frames. However, trajectories could be unstable due to the variations of speed and moving direction of individuals at different tracking duration, hence inaccurate collectiveness measurement. In this paper, we propose an approach to enhance the accuracy by stabilizing motions of individuals. The stabilization process is performed using smoothing methods. We evaluated our methods on a benchmark dataset, Collective Motion. Experimental results indicate that smoothing-based approaches outperform the original state-of-the-art approach.","PeriodicalId":349558,"journal":{"name":"2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124786250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Document Layout Analysis: A Maximum Homogeneous Region Approach 文档布局分析:一种最大同质区域方法
T. A. Tran, Khuong Nguyen-An, N. Vo
{"title":"Document Layout Analysis: A Maximum Homogeneous Region Approach","authors":"T. A. Tran, Khuong Nguyen-An, N. Vo","doi":"10.1109/MAPR.2018.8337515","DOIUrl":"https://doi.org/10.1109/MAPR.2018.8337515","url":null,"abstract":"This paper presents a method for document layout analysis. This method applies the analyzing of whitespace in maximum homogeneous regions. This method focuses on the balance between processing time and performance. It consists of two main stages: classification and segmentation. Firstly, by using the analysis of whitespace analysis on Maximum multi-layer horizontal homogeneous regions, the text and non-text elements are classified. Then, text regions are extracted by using mathematical morphology. Besides, non-text elements are classified into separators, tables, images via a machine learning approach. The proposed method's effectiveness is proved by the tests on UW-III (A1) datasets.","PeriodicalId":349558,"journal":{"name":"2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123386586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
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