Upgrading eigenspace-based prediction using null space and its application to path prediction

Subspace 2007 Workshop on ACCV2007, poster and slide ; Place : Tokyo, Japan ; Date : November 19, 200

Prediction of a path of pedestrian using combination of Eigenspace and null space

本稿では,固有空間を用いて学習した人物の歩行軌跡をもとに,ある時点までの人物の歩行軌跡から,それ以降の歩行軌跡を予測する手法について述べる.この予測は軌跡の固有空間への射影と逆射影に基づいている.しかし,予測された軌跡は画像中での人物の歩行軌跡としての特性を考慮していないため滑らかでない.そこで,本研究では固有空間に直交する零空間に属するベクトル(零ベクトル)を用いて,予測された軌跡を修正する手法を提案する.固有空間への射影と逆射影によって得られた予測軌跡に零ベクトルの線形和を加えることで,滑らかな軌跡へ修正する.零ベクトルの線形和の係数は,軌跡の滑らかさを評価関数とする最急降下法によって推定する.本手法を実際の人物の歩行軌跡に適用した結果を示す.This paper proposes a method for Eigenspace-based prediction of a person's path in future with current path. This method uses projection and back projection of the current path onto Eigenspace. However, the prediction does not take the nature of a path of pedestrian and results in non-smooth path. The proposed method modify the prediction by using null vectors in the orthocomplement of the learned Eigenspace and adding linear combinations of the null vectors to the prediction so that the path becomes smooth. Coe?cients of the linear combinations are computed by decent gradient method. Some experimental results on actual pathes are shown

Upgrading eigenspace-based prediction using null space and its application to path prediction

This paper proposes a method for an Eigenspace-based prediction of a vector with missing components by modifying a projection of conventional Eigenspace method, and demonstrates the application to the prediction of the path of a walking person. This modification is based on domain-specific knowledge of data, and a linear combination of vectors in the null space of Eigenspace is added so that a cost function of smoothness of path is minimized. Some experimental results on actual paths are shown to demonstrate how the proposed method works

Prediction of a path of pedestrian using combination of Eigenspace and null space

MIRU 2007 第10回 画像の認識・理解シンポジウム ポスター資料 ; 開催場所 : 広島市立大学, 広島 ; 開催日時:2007年7月30日～8月1

Percutaneous coronary intervention during the COVID-19 pandemic in Japan: Insights from the nationwide registration data.

[Background] Coronavirus disease 2019 (COVID-19) has negatively affected access to healthcare systems and treatment timelines. This study was designed to explore the impact of the COVID-19 pandemic on patients who underwent percutaneous coronary intervention (PCI). [Methods] From January 2019 to December 2020, 489, 001 patients from 1068 institutions were registered in the Japanese nationwide PCI (J-PCI) registry. We constructed generalized linear models to assess the difference in the daily number of patients and in-hospital outcomes between 2019 and 2020. [Findings] In total, 207 institutions (19·3%) had closed or restricted access during the first COVID-19 outbreak in May 2020; the number of closed or restricted institutions had plateaued at a median of 121 institutions (11·3%). The daily case volume of PCI significantly decreased in 2020 (by 6·7% compared with that in 2019; 95% confidence interval [CI], 6·2–7·2%; p < 0·001). Marked differences in the presentation of PCI patients were observed; more patients presented with ST-segment elevation myocardial infarction (18·3% vs. 17·5%; p < 0·001), acute heart failure (4·49% vs. 4·30%; p = 0·001), cardiogenic shock (3·79% vs. 3·45%; p < 0·001), and cardiopulmonary arrest (2·12% vs. 2·00%; p = 0·002) in 2020. The excess adjusted in-hospital mortality rate in patients treated in 2020 relative to those treated in 2019 was significant (adjusted odds ratio, 1·054; 95% CI, 1·004–1·107; p = 0·03). [Interpretation] While the number of patients who underwent PCI substantially decreased during the COVID-19 pandemic, more patients presented with high-risk characteristics and were associated with significantly higher adjusted in-hospital mortality. [Funding] The J-PCI registry is a registry led and supported by the Japanese Association of Cardiovascular Intervention and Therapeutics. The present study was supported by the Grant-in-Aid from the Ministry of Health and Labour (No. 20IA2002 and 21FA1015), the Grants-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (KAKENHI; No. 21K08064), and the Japan Agency for Medical Research and Development (No. 17ek0210097h000)

Comparative study of path nomalizations for path prediction

This paper disccusses two methods on normalization of a sample path for predicting paths of a pedestrian by using eigenspace. A path of a person is defined as a sequence of successive coordinates of the person over frames and represented the path as a vector with 2M elements of M number of coordinates. A problem of their prediction is that the method is based on subspace. To make a subspace from sample paths, all paths need to be normalized and resampled such that a path vector has the same number of elements. Because different sample paths have different number of frames. In this paper, we apply a normalization method using DP matching and discuss results of two predictions : resampling and DP

Comparative study of path nomalizations for path prediction

The 14th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2008), Slide ; Place : Beppu, Oita, Japan ; Date : January 23-26, 200