Person following and mobile camera localization using particle filters

Abstrac

Adaptive Feature Selection for Object Tracking with Particle Filter

International audienceObject tracking is an important topic in the field of computer vision. Commonly used color-based trackers are based on a fixed set of color features such as RGB or HSV and, as a result, fail to adapt to changing illumination conditions and background clutter. These drawbacks can be overcome to an extent by using an adaptive framework which selects for each frame of a sequence the features that best discriminate the object from the background. In this paper, we use such an adaptive feature selection method embedded into a particle filter mechanism and show that our tracking method is robust to lighting changes and background distractions. Different experiments also show that the proposed method outperform other approaches

Human Gait Recognition from Motion Capture Data in Signature Poses

Most contribution to the field of structure-based human gait recognition has been done through design of extraordinary gait features. Many research groups that address this topic introduce a unique combination of gait features, select a couple of well-known object classiers, and test some variations of their methods on their custom Kinect databases. For a practical system, it is not necessary to invent an ideal gait feature -- there have been many good geometric features designed -- but to smartly process the data there are at our disposal. This work proposes a gait recognition method without design of novel gait features; instead, we suggest an effective and highly efficient way of processing known types of features. Our method extracts a couple of joint angles from two signature poses within a gait cycle to form a gait pattern descriptor, and classifies the query subject by the baseline 1-NN classier. Not only are these poses distinctive enough, they also rarely accommodate motion irregularities that would result in confusion of identities. We experimentally demonstrate that our gait recognition method outperforms other relevant methods in terms of recognition rate and computational complexity. Evaluations were performed on an experimental database that precisely simulates street-level video surveillance environment

Sex and Gender-Based Women\u27s Health: A Practical Guide for Primary Care - A Resource for Learning and Teaching

TOP WINNER Background: Patients expect comprehensive, gender-specific care; yet studies reveal that few residency programs in internal medicine provide dedicated training in women’s health and gender-based medicine. Further, graduates are unable to demonstrate competency to care for female and gender-diverse patients. Objectives: To produce a sex and gender-based women’s health curriculum, written explicitly for primary care providers to guide the care of women and gender-diverse patients, and to be used as a curriculum to educate learners. Methods: In collaboration with Springer Nature, development of this online and inprint textbook began in 2017. Topics were chosen with input from women’s health experts and are tailored to gender-based conditions commonly evaluated, diagnosed, and/or managed in the primary care setting. Authors were then recruited nationally for each topic. Using evidenced-based medicine principles, chapters were formatted for consistency to include the epidemiology, physiology/pathophysiology, clinical manifestations, differential diagnosis, diagnostic approach, and treatment for each topic, when appropriate. Each chapter has clear, measurable learning objectives, summary statements, and multiplechoice questions with annotated answers to check understanding and help earn CME and MOC credit. With 39 chapters and \u3e600 pages, sections are comprehensive and include Breast and Gynecologic Health and Disease, Obstetric Medicine, Chronic Pain Disorders, Mental Health and Trauma, LGBTQ Health, Common Medical Conditions (osteoporosis, cardiovascular disease), and Foundations of Women’s Health, which highlights the history, disparities, and future of women’s and gender-based healthcare. Conclusions/Impact: This is the first comprehensive curricular resource written by clinical women’s health physicians, using the most up-to-date evidence, clinical guidelines, expert opinion, and clinical pearls. Our goal is to provide a guide that can serve as a quick point-of-care clinical reference for a specific topic or as a longitudinal curriculum for learners in any primary care discipline, especially programs where women’s health and gender-specific curricula and champions are sparse.https://jdc.jefferson.edu/sexandgenderhealth/1030/thumbnail.jp

An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods

As a contribution to reproducible research, this paper presents a framework and a database to improve the development, evaluation and comparison of methods for gait recognition from Motion Capture (MoCap) data. The evaluation framework provides implementation details and source codes of state-of-the-art human-interpretable geometric features as well as our own approaches where gait features are learned by a modification of Fisher's Linear Discriminant Analysis with the Maximum Margin Criterion, and by a combination of Principal Component Analysis and Linear Discriminant Analysis. It includes a description and source codes of a mechanism for evaluating four class separability coefficients of feature space and four rank-based classifier performance metrics. This framework also contains a tool for learning a custom classifier and for classifying a custom query on a custom gallery. We provide an experimental database along with source codes for its extraction from the general CMU MoCap database

Walker-Independent Features for Gait Recognition from Motion Capture Data

MoCap-based human identification, as a pattern recognition discipline, can be optimized using a machine learning approach. Yet in some applications such as video surveillance new identities can appear on the fly and labeled data for all encountered people may not always be available. This work introduces the concept of learning walker-independent gait features directly from raw joint coordinates by a modification of the Fisher’s Linear Discriminant Analysis with Maximum Margin Criterion. Our new approach shows not only that these features can discriminate different people than who they are learned on, but also that the number of learning identities can be much smaller than the number of walkers encountered in the real operation

Benchmark RGB-D Gait Datasets: A Systematic Review

Human motion analysis has proven to be a great source of information for a wide range of applications. Several approaches for a detailed and accurate motion analysis have been proposed in the literature, as well as an almost proportional number of dedicated datasets. The relatively recent arrival of depth sensors contributed to an increasing interest in this research area and also to the emergence of a new type of motion datasets. This work focuses on a systematic review of publicly available depth-based datasets, encompassing human gait data which is used for person recognition and/or classification purposes. We have conducted this systematic review using the Scopus database. The herein presented survey, which to the best of our knowledge is the first one dedicated to this type of datasets, is intended to inform and aid researchers on the selection of the most suitable datasets to develop, test and compare their algorithms. (c) Springer Nature Switzerland AG 2019