Dynamic feature detection using virtual correction and camera oscillations

Visual SLAM algorithms exploit natural scene features to infer the camera motion and build a map of a static environment. In this paper, we relax the severe assumption of a static scene to allow for the detection and deletion of dynamic points. A new "virtual correction" method is introduced which serves to detect the dynamic points by checking the re-projection error of the points before and after the virtual measurement update. It can also recover the erroneously excluded useful features, particularly the distant points which may be deleted because of the change in its position after new measurement observation. Deliberate camera oscillations are also used to improve the VSLAM accuracy and the camera observability. The simulation results showed the effectiveness of the virtual correction when combined with camera oscillation in recovering the misclassified features and detecting the dynamic features even in difficult scenarios

Camera oscillation pattern for VSLAM: translational versus rotational

Visual SLAM algorithms exploit natural scene features to infer the camera motion and build a map of the environment landmarks. SLAM algorithm has two interrelated processes localization and mapping. For accurate localization, we need the features location estimates to converge quickly. On the other hand, to build an accurate map, we need accurate localization. Recently, a biologically inspired approach exploits deliberate camera oscillation has been used to improve the convergence speed of depth estimate. In this paper, we explore the effect of camera oscillation pattern on the accuracy of VSLAM. Two main oscillation patterns are used for distance estimation: translational and rotational. Experiments, using static and moving robot, are made to explore the effect of these oscillation patterns on the VSLAM performance

2014～2015年度 教育研究高度化促進費 研究成果報告書「わが国の新たな情報法制の定立のための比較法研究と理解促進の取組」

目次1.研究の目的と研究成果の概要2.研究成果(1) シンポジウム(2) 論文・第一論文 髙野一彦「新しい法制度に対応したコンプライアンス・プログラムの定立を」衆知2017.1-2 号、PHP研究所、2016年、66～69頁・第二論文 髙野一彦 ｢情報危機管理とビッグデータ : わが国の個人情報保護法制への提言と企業コンプライアンス｣、関西大学社会安全学部編『リスク管理のための社会安全学』ミネルヴァ書房、2015年、21～46頁・第三論文 新保史生「改正個人情報保護法の論点」憲法学会、憲法研究48号、2016年、29-55頁・第四論文 河野和宏「大学生に対する違法動画視聴の防止対策に関する一検討 : 不正のトライアングル理論と状況的犯罪予防論からの検討」電子情報通信学会技術研究報告、SSS2018-15、2018年9～12頁・第五論文 新井健介・河野和宏・馬場口登｢推薦対象の属性から構築した階層構造を用いたTF-IDF法による匿名化処理｣電子情報通信学会技術研究報告vol. 115、no. 479、EMM2015-81、2016年、31～36頁・第六論文 新井健介・河野和宏・馬場口登「TF-IDF法によるユーザへの情報推薦のための匿名化処理」電子情報通信学会技術研究報告vol. 115、no. 38、IT2015-10、EMM2015-10、2015年、51～56頁3.謝

Security Analysis of Anonymous Communication System 3-Mode Net Against Collaborating Nodes

This paper analyzes the security of an anonymous communication system 3-Mode Net (3MN) against collaborating relay nodes. We evaluate the anonymity of a message sender under the situation that some relay nodes collaborate each other to find out the message sender. As in the case of Crowds, we define the measure of the anonymity of the message sender as the probability that the first immediate predecessor among the immediate predecessors of all collaborating relay nodes is in fact the message sender. This paper gives an explicit formula for this probability. Some numerical examples are also presented.APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference. 4-7 October 2009. Sapporo, Japan. Oral session: Signal Processing and Security in Communications (5 October 2009)

MOTION ESTIMATION AND DETECTION OF COMPLEX OBJECT BY ANALYZING RESAMPLED MOVEMENTS OF PARTS

A moving object that has many complex moving parts is very hard to detect and its motion is not easy to estimate. In this paper, we present a new technique for motion estimation and detection of moving complex objects by analyzing the resampled motions of the parts of objects. The Kalman filter is used to track all resampled movements and the tracked routes are classified into groups that share the same fundamental movements. Our simulation show that recall of motion estimation and detection is approximately 0.8, while the computation drops exponentially. 1

Automatic story segmentation of closed-caption text for semantic content analysis of broadcasted sports video

Sports videos can be characterized as a sequence of recurrent semantic story units. Storing sports videos in this story-unit-based form will lead to develop an intelligent content-based retrieval, browsing, and summarization system. The storage requires segmentation of videos and semantic understanding of each segment. Since transcribed broadcasted video speech, the closed-caption text, can be the useful information source for semantic indexing of each story unit, this paper proposes a method to automatically segment the closed-caption text of sports videos into the semantic units. The proposed method firstly tries to segment the speech transcript into the scene units, a set of which composes a story unit, in a probabilistic framework based on Bayesian networks. Finding the boundaries of the set of the scene units enables us to generate the story units in the close-caption. In this paper, we discuss some experimental results and the potentiality for utilizing them for indexing of the video and speech summarization.