Study Of Human Activity In Video Data With An Emphasis On View-invariance

The perception and understanding of human motion and action is an important area of research in computer vision that plays a crucial role in various applications such as surveillance, HCI, ergonomics, etc. In this thesis, we focus on the recognition of actions in the case of varying viewpoints and different and unknown camera intrinsic parameters. The challenges to be addressed include perspective distortions, differences in viewpoints, anthropometric variations, and the large degrees of freedom of articulated bodies. In addition, we are interested in methods that require little or no training. The current solutions to action recognition usually assume that there is a huge dataset of actions available so that a classifier can be trained. However, this means that in order to define a new action, the user has to record a number of videos from different viewpoints with varying camera intrinsic parameters and then retrain the classifier, which is not very practical from a development point of view. We propose algorithms that overcome these challenges and require just a few instances of the action from any viewpoint with any intrinsic camera parameters. Our first algorithm is based on the rank constraint on the family of planar homographies associated with triplets of body points. We represent action as a sequence of poses, and decompose the pose into triplets. Therefore, the pose transition is broken down into a set of movement of body point planes. In this way, we transform the non-rigid motion of the body points into a rigid motion of body point iii planes. We use the fact that the family of homographies associated with two identical poses would have rank 4 to gauge similarity of the pose between two subjects, observed by different perspective cameras and from different viewpoints. This method requires only one instance of the action. We then show that it is possible to extend the concept of triplets to line segments. In particular, we establish that if we look at the movement of line segments instead of triplets, we have more redundancy in data thus leading to better results. We demonstrate this concept on “fundamental ratios.” We decompose a human body pose into line segments instead of triplets and look at set of movement of line segments. This method needs only three instances of the action. If a larger dataset is available, we can also apply weighting on line segments for better accuracy. The last method is based on the concept of “Projective Depth”. Given a plane, we can find the relative depth of a point relative to the given plane. We propose three different ways of using “projective depth:” (i) Triplets - the three points of a triplet along with the epipole defines the plane and the movement of points relative to these body planes can be used to recognize actions; (ii) Ground plane - if we are able to extract the ground plane, we can find the “projective depth” of the body points with respect to it. Therefore, the problem of action recognition would translate to curve matching; and (iii) Mirror person - We can use the mirror view of the person to extract mirror symmetric planes. This method also needs only one instance of the action. Extensive experiments are reported on testing view invariance, robustness to noisy localization and occlusions of body points, and action recognition. The experimental results are very promising and demonstrate the efficiency of our proposed invariants. i

Calibration-based minimalistic multi-exposure digital sensor camera robust linear high dynamic range enhancement technique demonstration

Demonstrated for a digital image sensor-based camera is a calibration target optimized method for finding the Camera Response Function (CRF). The proposed method uses localized known target zone pixel outputs spatial averaging and histogram analysis for saturated pixel detection. Using the proposed CRF generation method with a 87 dB High Dynamic Range (HDR) silicon CMOS image sensor camera viewing a 90 dB HDR calibration target, experimentally produced is a non-linear CRF with a limited 40 dB linear CRF zone. Next, a 78 dB test target is deployed to test the camera with this measured CRF and its restricted 40 dB zone. By engaging the proposed minimal exposures, weighting free, multi-exposure imaging method with 2 images, demonstrated is a highly robust recovery of the test target. In addition, the 78 dB test target recovery with 16 individual DR value patches stays robust over a factor of 20 change in test target illumination lighting. In comparison, a non-robust test target image recovery is produced by 5 leading prior-art multi-exposure HDR recovery algorithms using 16 images having 16 different exposure times, with each consecutive image having a sensor dwell time increasing by a factor of 2. Further validation of the proposed HDR image recovery method is provided using two additional experiments, the first using a 78 dB calibrated target combined with a natural indoor scene to form a hybrid design target and a second experiment using an uncalibrated indoor natural scene. The proposed technique applies to all digital image sensor-based cameras having exposure time and illumination controls. In addition, the proposed methods apply to various sensor technologies, spectral bands, and imaging applications

Robust testing of displays using the extreme linear dynamic range CAOS camera

Proposed and demonstrated for the first time is robust testing of optical displays using the extreme linear Dynamic Range (DR) CAOS camera. Experiments highlight accurate and repeatable CAOS camera-based testing of standard 8-bit (i.e., 48 dB DR) and modified DR 10-bit (i.e., 60 dB DR) computer Liquid Crystal Displays (LCDs). Results are compared with CMOS camera and light meter-based LCD testing highlighting the robustness of the CAOS camera readings

Residential rooftop solar panel adoption behavior: Bibliometric analysis of the past and future trends

This study reviews residents' behavioral adoption of rooftop solar photovoltaics (solar PV). Solar PV imparts many benefits towards the environment, economic and social development. However, there has been no comprehensive understanding of knowledge structure in solar PV adoption among households in the literature. Through a bibliometric approach, 564 publications on residents’ adoption of solar PV were retrieved from the Web of Science (WoS). A co-citation and co-word analysis were performed to uncover past and predict future trends in this regard. The analysis produces significant themes related to residents' diffusion innovation adoption and motivation/predictors toward solar PV. This review contributes to the fundamental understanding of residents' critical determinants of solar PV adoption. Theory and practical implications are discussed

Role of sustainable development goals in advancing the circular economy: A state-of-the-art review on past, present and future directions

The purpose of this study is to review the relationship between the highly anticipated concept of circular economy (CE) and sustainable development goals (SDGs). These two sustainability principles have transformed organizations and countries in their quest to achieve sustainable development. Despite their importance to the business and corporate realm, the discussion of these two concepts has been developed in silos, arbitrarily connected. Through a bibliometric approach, this study reviewed 226 journal publications and 16,008 cited references from the Web of Science (WoS) to understand the past, present and future trends of the two concepts and their impact on the sustainability development. The bibliometric approach of citation, co-citation and co-word analysis uncovers the relevant and significant themes and research streams. Theoretical and practical implications were discussed within the broader business and governance perspective to develop a substantial triple bottom line in creating a sustainable future for civil society

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Human Action Recognition In Video Data Using Invariant Characteristic Vectors

We introduce the concept of the \u27characteristic vector\u27 as an invariant vector associated with a set of freely moving points relative to a plane. We show that if the motion of two sets of points differ only up to a similarity transformation, then the elements of the characteristic vector differ up to scale regardless of viewing directions and cameras. Furthermore, this invariant vector is given by any arbitrary homography that is consistent with epipolar geometry. The characteristic vector of moving points can thus be used to recognize the transitions of a set of points in an articulated body during the course of an action regardless of the camera orientation and parameters. Our extensive experimental results on both motion capture data and real data indicates very good performance. © 2012 IEEE

Motion Retrieval Using Consistency Of Epipolar Geometry

In this paper, we present an efficient method for motion retrieval method based on the consistency of the homographies with the epipolar geometry. We treat the body pose as body point triplets and use the fact that each homography obtained from corresponding body point triplets should be consistent with epipolar geometry to estimate the similarity of two poses. We show that our method is invariant to camera internal parameters and viewpoint. Experiments are performed on the CMU MoCap dataset, and IXMAS dataset testing testing view-invariance, and action recognition. The results demonstrate that our method can accurately identify human action from video sequences when they are observed from totally different viewpoints with different camera parameters

Robust Auto-Calibration Of A Ptz Camera With Non-Overlapping Fov

We consider the problem of auto-calibration of cameras, which are fixed in location but are free to rotate while changing their internal parameters by zooming. Our method is based on line correspondences between two views, which may have non-overlapping field of view. Camera calibration from images having non-overlapping field of view is the basic motivation behind this research. The key observation is that the planes formed by the optic center and the line correspondences are really the same plane. We use this fact together with the orthonormality constraint of the rotation matrix to estimate the unknown camera parameters. We show experimental results on synthetic and real data, and analyze the accuracy and stability of our method. © 2008 IEEE