Gaıt-Based Gender Classıfıcatıon Usıng Neutral And Non-Neutral Gaıt Sequences

Nötr veya Nötr Olmayan Ardaşık Yürüyüş Tarzlarından Davranış-bağımlı Cinsiyet Klasifikasyonu Biometrik sistem bireyle özleĢik en çok göze çarpan bir özellik veya niteliğe dayalı bir vasıf kullanılarak bireyin tanımlanmasını sağlar. Biometric tanımlayıcılar genellikle davranıĢsal özelliklere karĢı fizyolojik özellikler olarak kategorize edilir. Fizyolojik özellikler Ģahsın parmak izi, avuç içi damarlar, yüz tanıma gibi vücudun yapısal özellikleriyle ilgili olmasına karĢın, Ģahsın davranıĢsal özellikleri yürüme tarzı, imzası ve sesiyle ilgili vasıflarıdır. YürüyüĢ tarzı biometrik tanımlama yöntemi ile kiĢilerin erkek veya kadın olduğunun tanımlamasında kullanılacağı gibi kiĢilerin yürüyüĢ tarzları, yetkisiz kiĢilerin ve cinsiyetlerin belirlenmesi, ve yürüme veya yürümeye bağlı anormalliklerin tespiti gibi farklı uygulama alanlarında kullanılabilir. Bu tezde, kiĢilerin yürüyüĢ özelliklerine göre cinsiyet sınıflandırması yapan bir yöntem önerilmiĢtir. Nötr yürüyüĢ dizilerinin yanı sıra palto/manto giyme (CW) ve çanta taĢıma (CB) gibi nötr olmayan yürüyüĢ tarzlarından kaynaklanan tanımlama sorunları araĢtırılmıĢtır. Cinsiyet sınıflandırma amacıyla farklı yürüyüĢ tarzı dizinlerinin araĢtırılması ve denemelerinin yapılması üzerinde durulmuĢtur. Sayısal denemeler Casia B veritabanında mevcut değiĢik yürüyüĢ tarzları üzerinde çok sayıda denek üzerinde yapılmıĢtır. Bu veritabaında 11 farklı görünüm açılarından kaydedilen 124 kiĢi (31 kadın ve 93 erkek) bulunmaktadır. Her bir denek için, 6 nötr (Nu), 2 adet manto/palto giyme (CW) ve 2 adet çanta taĢıma (CB) olmak üzere 10 yürüme dizini bulunmaktadır. Önerilen yöntemin ilk bölümünde bir çerçeveli görüntüden arka planı çıkarma yöntemi kullanarak sırasal çerçeveli görüntüler ile arka planı arasındaki farkın hesaplaması üzerinde durulmuĢtur. Ġkinci bölümde YürüyüĢ Enerjisi (Gait Energy) görüntü özelliklikleri yardımıyla sınıflandırma yöntemi incelenmiĢtir. Son olarak bu çalıĢmada bir sınıflandırma aracı olarak Yürüme Enerjisi Görüntü (Gait Energy Image) ve Rastgele Yürüme Enerji Görüntü (Gait Entropy Enerji Image, GEnEI) yöntemlri uygulanmıĢtır. Wavelet Transformasyon tekniği ve GEnEI yöntemi kullanılarakveritabanından üç farklı yürüyüĢ tarzı özellikli görüntü grubu kurgulanmıĢtır. Bu yürüyüĢ tarzı özellikli görüntü grupları: (i) YaklaĢık Katsayı Rastgele Yürüme Enerji Görüntü (Approximate coefficient Energy Image, AGEnEI), (ii) Diksel Katsayı Rastgele Yürüme Enerji Görüntü (Vertical coefficient Energy Image, VGEnEI), ve (iii) her ikisinin birleĢkesi olan YaklaĢık ve Diksel Katsayı Yürüme Enerji Görüntü (Approximate coefficient Energy Image and Vertical coefficient Energy Image, AVGEnEI). Yukarıda belirtilen görüntüleme iĢlemlerinin iĢlevliliğinin denemesi için k-derece yakın komĢu (k-Neraest Neighboor, k-NN) ve destek vector makinası (Support vector Machine, SVM) olarak bilinen yöntemler önerilmiĢtir. Ayrıca yukarıda belirtilen üç tür enerji görüntü yöntemi birleĢtirme tabanlı karar verme (fuse-based decirion level fusion) yöntemi kullanılarak da denenmiĢtir. Sınıflandırmada k-NN yöntemi ile Nu gait dizinleri için AGEnEI % 97 lik ergitme seviyesini (fusion level), VGEnEI CB dizinleri için 91.4% lik ergitme seviyesini, ve AGEnEI CW dizinleri için %83.4 ergitme seviyesi sonuçları bulunmuĢtur. k=1, 3 ve 5 sayıları ile belirlenen üç ayrı özellik grubu arasında k=1 dikkate değer ergitme seviyesi sonuçları vermiĢtir. Her üç enerji görüntüleme yöntemi (Energy Entropy Image) „Decision-fusion‟ yöntemi ile birleĢtirildiğinde (fused) ergitme dereceleri Nu için %99.8, CB için %92.2 ve for CW için 86.3% dir. Bu sonuçlar her bir özelliğin ayrı ayrı ele alındığı durumunda elede edilen sonuçlardan daha iyi olduğu dikkate değerdir.

The Meaning of Action:a review on action recognition and mapping

In this paper, we analyze the different approaches taken to date within the computer vision, robotics and artificial intelligence communities for the representation, recognition, synthesis and understanding of action. We deal with action at different levels of complexity and provide the reader with the necessary related literature references. We put the literature references further into context and outline a possible interpretation of action by taking into account the different aspects of action recognition, action synthesis and task-level planning

Training perceptual anticipation in sports: the case of the penalty kick in football

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Psicología, Departamento de Psicología Básica. Fecha de lectura: 16-10-201

Statistical and Dynamical Modeling of Riemannian Trajectories with Application to Human Movement Analysis

abstract: The data explosion in the past decade is in part due to the widespread use of rich sensors that measure various physical phenomenon -- gyroscopes that measure orientation in phones and fitness devices, the Microsoft Kinect which measures depth information, etc. A typical application requires inferring the underlying physical phenomenon from data, which is done using machine learning. A fundamental assumption in training models is that the data is Euclidean, i.e. the metric is the standard Euclidean distance governed by the L-2 norm. However in many cases this assumption is violated, when the data lies on non Euclidean spaces such as Riemannian manifolds. While the underlying geometry accounts for the non-linearity, accurate analysis of human activity also requires temporal information to be taken into account. Human movement has a natural interpretation as a trajectory on the underlying feature manifold, as it evolves smoothly in time. A commonly occurring theme in many emerging problems is the need to \emph{represent, compare, and manipulate} such trajectories in a manner that respects the geometric constraints. This dissertation is a comprehensive treatise on modeling Riemannian trajectories to understand and exploit their statistical and dynamical properties. Such properties allow us to formulate novel representations for Riemannian trajectories. For example, the physical constraints on human movement are rarely considered, which results in an unnecessarily large space of features, making search, classification and other applications more complicated. Exploiting statistical properties can help us understand the \emph{true} space of such trajectories. In applications such as stroke rehabilitation where there is a need to differentiate between very similar kinds of movement, dynamical properties can be much more effective. In this regard, we propose a generalization to the Lyapunov exponent to Riemannian manifolds and show its effectiveness for human activity analysis. The theory developed in this thesis naturally leads to several benefits in areas such as data mining, compression, dimensionality reduction, classification, and regression.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Ecomorphological Variation in Three Species of Cybotoid Anoles

© 2018 by The Herpetologists' League, Inc. Caribbean Anolis lizards exhibit a complex suite of ecological, morphological, and behavioral traits that allow their specialization to particular microhabitats. These microhabitat specialists, called ecomorphs, have independently evolved on the four islands of the Greater Antilles, and diversification among anole ecomorphs has been the focus of many studies. Yet, habitat specialization has also occurred among species within the same ecomorph group. Here, we examined ecological, morphological, and behavioral divergence in three Hispaniolan trunk-ground species, the cybotoid anoles: Anolis cybotes, A. marcanoi, and A. longitibialis. We found differences in limb morphology, locomotor behavior, and perch use among the three cybotoid species that mirror differences across the ecomorphs. Among these species of cybotoids, those that have longer limbs tend to move less frequently, occupy broader perches, and have smaller fourth toes with fewer lamellae. We also observed that the species with greater male-biased size dimorphism had larger heads, smaller dewlaps, and smaller testes. These results are consistent with the predictions of sexual selection theory, in that species with large male body size may have larger heads because of increased male-male combat, and smaller testes potentially attributable to a trade-off between pre- and postcopulatory selection. Overall, our study suggests that a combination of local adaptation to different structural habitats and sexual selection might produce ecomorphological diversification within cybotoid anoles of the same ecomorph group

Articulated human tracking and behavioural analysis in video sequences

Recently, there has been a dramatic growth of interest in the observation and tracking of human subjects through video sequences. Arguably, the principal impetus has come from the perceived demand for technological surveillance, however applications in entertainment, intelligent domiciles and medicine are also increasing. This thesis examines human articulated tracking and the classi cation of human movement, rst separately and then as a sequential process. First, this thesis considers the development and training of a 3D model of human body structure and dynamics. To process video sequences, an observation model is also designed with a multi-component likelihood based on edge, silhouette and colour. This is de ned on the articulated limbs, and visible from a single or multiple cameras, each of which may be calibrated from that sequence. Second, for behavioural analysis, we develop a methodology in which actions and activities are described by semantic labels generated from a Movement Cluster Model (MCM). Third, a Hierarchical Partitioned Particle Filter (HPPF) was developed for human tracking that allows multi-level parameter search consistent with the body structure. This tracker relies on the articulated motion prediction provided by the MCM at pose or limb level. Fourth, tracking and movement analysis are integrated to generate a probabilistic activity description with action labels. The implemented algorithms for tracking and behavioural analysis are tested extensively and independently against ground truth on human tracking and surveillance datasets. Dynamic models are shown to predict and generate synthetic motion, while MCM recovers both periodic and non-periodic activities, de ned either on the whole body or at the limb level. Tracking results are comparable with the state of the art, however the integrated behaviour analysis adds to the value of the approach.Overseas Research Students Awards Scheme (ORSAS

Human gait identification and analysis

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Human gait identification has become an active area of research due to increased security requirements. Human gait identification is a potential new tool for identifying individuals beyond traditional methods. The emergence of motion capture techniques provided a chance of high accuracy in identification because completely recorded gait information can be recorded compared with security cameras. The aim of this research was to build a practical method of gait identification and investigate the individual characteristics of gait. For this purpose, a gait identification approach was proposed, identification results were compared by different methods, and several studies about the individual characteristics of gait were performed. This research included the following: (1) a novel, effective set of gait features were proposed; (2) gait signatures were extracted by three different methods: statistical method, principal component analysis, and Fourier expansion method; (3) gait identification results were compared by these different methods; (4) two indicators were proposed to evaluate gait features for identification; (5) novel and clear definitions of gait phases and gait cycle were proposed; (6) gait features were investigated by gait phases; (7) principal component analysis and the fixing root method were used to elucidate which features were used to represent gait and why; (8) gait similarity was investigated; (9) gait attractiveness was investigated. This research proposed an efficient framework for identifying individuals from gait via a novel feature set based on 3D motion capture data. A novel evaluating method of gait signatures for identification was proposed. Three different gait signature extraction methods were applied and compared. The average identification rate was over 93%, with the best result close to 100%. This research also proposed a novel dividing method of gait phases, and the different appearances of gait features in eight gait phases were investigated. This research identified the similarities and asymmetric appearances between left body movement and right body movement in gait based on the proposed gait phase dividing method. This research also initiated an analysing method for gait features extraction by the fixing root method. A prediction model of gait attractiveness was built with reasonable accuracy by principal component analysis and linear regression of natural logarithm of parameters. A systematic relationship was observed between the motions of individual markers and the attractiveness ratings. The lower legs and feet were extracted as features of attractiveness by the fixing root method. As an extension of gait research, human seated motion was also investigated.This study is funded by the Dorothy Hodgkin Postgraduate Awards and Beijing East Gallery Co. Ltd

Automatic visual detection of human behavior: a review from 2000 to 2014

Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.This work is funded by the Portuguese Foundation for Science and Technology (FCT - Fundacao para a Ciencia e a Tecnologia) under research Grant SFRH/BD/84939/2012

NaturalWalk: An Anatomy-based Synthesizer for Human Walking Motions

We present a novel data-driven approach for synthesizing human gait motions with individual style characteristics and natural appearance. Our approach is based on the concept of a motion signature that captures the essential characteristic of an individual walking motion. For each joint angle our motion model consists of a shape template and feature functions that describe the variation of that shape with the stride length. For the synthesis of a walking motion, the feature functions are evaluated for a desired stride length. Then the templates are adapted to match the computed features and used as progressions for the joint angles of the skeleton. We demonstrate our data driven approach using motion data captured from 12 individuals. We report on an experiment showing that the synthesized motions have a natural appearance and maintain the individual style.:1. Introduction 2. Related Work 3. Preliminaries 3.1 Mathematics of motion 3.2 Walking motions 4. Data acquisition and analysis 5. Shape templates and feature functions 5.1 Definition of template functions 5.2 Continuous representation of template functions 5.3 Building the feature functions 6. Motion Generation 6.1 Adaption of template functions 6.2 Computing the poses 7 Experimental Results 7.1 Numerical Evaluation 7.2 User Study Acknowledgment Reference