Human Posture Recognition in Video Sequence

International audienceThis paper presents a new approach to recognize human postures in video sequences comparing two methods. We first describe these two methods based on 2D appearances. The first one uses projections of moving pixels on the reference axis. The second method decomposes the human silhouette into blocks and learns 2D posture appearances through PCA. Then we use 3D model of posture to make the previous methods independent of the camera position. At the end we give some preliminary results and conclude on the effectiveness of this approach

Master of Science

thesisCurrently, few methods exist to accurately model a human motion inside a monitored area. Most of the approaches that exist depend on some kind of boolean data from sensors that tell the presence or absence of person a at a given instant of time near a particular sensor. Using that information, some systems can then track a person across the area at di erent timestamps. Furthermore, for most existing approaches, the accuracy drops rapidly as the number of persons in the image increases. The sensors used in such settings are usually expensive. Not much work has been done to build a similar system based on inexpensive radio sensors. As there is no way for our radio sensors to provide information as to whether a person is present at a location, we need to extract it from the data using computer vision and machine learning techniques. However, it is not easy in such a system to model the noise component accurately. Therefore, we provide a probabilistic model to decide whether a detected blob is noise or an actual person. In our work, we exploit the fact that images do not change by much between successive timeframes and use this to detect and track multiple persons in a monitored area with a reasonably high accuracy. We use location and count of persons in historical images, and their similarity with the current image to calculate the new locations and count

Hand Gesture Recognition for Sign Language Transcription

Sign Language is a language which allows mute people to communicate with other mute or non-mute people. The benefits provided by this language, however, disappear when one of the members of a group does not know Sign Language and a conversation starts using that language. In this document, I present a system that takes advantage of Convolutional Neural Networks to recognize hand letter and number gestures from American Sign Language based on depth images captured by the Kinect camera. In addition, as a byproduct of these research efforts, I collected a new dataset of depth images of American Sign Language letters and numbers, and I compared the presented method for image recognition against a similar dataset but for Vietnamese Sign Language. Finally, I present how this work supports my ideas for the future work on a complete system for Sign Language transcription

Introdução à Análise de Movimento usando Visão Computacional

Pretende-se com este trabalho fazer uma introdução ao que tem vindo a ser realizado no domínio do seguimento e análise de movimento recorrendo a visão computacional.Assim no primeiro capítulo deste relatório faremos referência aos vários tipos de movimento e analisaremos as fases que compõem um sistema comum de captura e análise de movimento, descrevendo sucintamente alguns trabalhos realizados nesta área.Seguidamente, no segundo capítulo, faremos uma apresentação mais detalhada da área do seguimento e análise de movimento humano de corpo inteiro; nomeadamente, no reconhecimento da pose e do reconhecimento do andar e de gestos.Finalmente, no terceiro e último capítulo, daremos ênfase à análise de imagem médica e exemplificaremos, sumariamente, algumas das suas aplicações.With this work we intend to introduce what has been done in the domain of tracking and motion analysis by using computational vision.Therefore in the first chapter of this report we will refer the various types of motion, and analyse the steps that compose a general system of movement capture and analysis, by succinctly describing some works done in this field.Then, in the second chapter we will do a more detailed study about the area of human entire body tracking and motion analysis; namely, in pose recognition and in the recognition of gait and gestures.Finally, in the third and last chapter, emphasis will be given to the medical images analysis and we will summarily exemplify some of its applications

3D model-based human motion capture

Master'sMASTER OF ENGINEERIN

Gait analysis and recognition for automated visual surveillance

Human motion analysis has received a great attention from researchers in the last decade due to its potential use in different applications such as automated visual surveillance. This field of research focuses on the perception and recognition of human activities, including people identification. We explore a new approach for walking pedestrian detection in an unconstrained outdoor environment. The proposed algorithm is based on gait motion as the rhythm of the footprint pattern of walking people is considered the stable and characteristic feature for the classification of moving objects. The novelty of our approach is motivated by the latest research for people identification using gait. The experimental results confirmed the robustness of our method to discriminate between single walking subject, groups of people and vehicles with a successful detection rate of 100%. Furthermore, the results revealed the potential of our method to extend visual surveillance systems to recognize walking people. Furthermore, we propose a new approach to extract human joints (vertex positions) using a model-based method. The spatial templates describing the human gait motion are produced via gait analysis performed on data collected from manual labeling. The Elliptic Fourier Descriptors are used to represent the motion models in a parametric form. The heel strike data is exploited to reduce the dimensionality of the parametric models. People walk normal to the viewing plane, as major gait information is available in a sagittal view. The ankle, knee and hip joints are successfully extracted with high accuracy for indoor and outdoor data. In this way, we have established a baseline analysis which can be deployed in recognition, marker-less analysis and other areas. The experimental results confirmed the robustness of the model-based approach to recognise walking subjects with a correct classification rate of 95% using purely the dynamic features derived from the joint motion. Therefore, this confirms the early psychological theories claiming that the discriminative features for motion perception and people recognition are embedded in gait kinematics. Furthermore, to quantify the intrusive nature of gait recognition we explore the effects of the different covariate factors on the performance of gait recognition. The covariate factors include footwear, clothing, carrying conditions and walking speed. As far as the author can determine, this is the first major study of its kind in this field to analyse the covariate factors using a model-based method.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Application of CBIR techniques for the purpose of biometric identification based on human gait

Intenzivan razvoj informaciono-komunikacionih tehnologija otvorio je vrata primeni biometrijskih tehnologija u menadžmentu identiteta. Biometrijski modalitet koji ima veliki potencijal za primenu u praksi je ljudski hod. Njega odlikuju neinvazivnost i neintruzivnost. Ovakve osobine posebno pogoduju primeni u uslovima tehnologije prismotre. Zahvaljujući tome, ovaj biometrijski modalitet tokom prethodnih godina izaziva veliko interesovanje akademske zajednice. Ovo interesovanje rezultiralo je razvojem velikog broja pristupa za prepoznavanje osoba na osnovu hoda. Uprkos tome, primena biometrijskih tehnologija zasnovanih na ljudskom hodu u praksi i dalje zaostaje za dobro ustanovljenim modalitetima poput otiska prsta, lica ili glasa. Glavni razlog je nedostatak odgovarajućeg pristupa koji bi omogućio stabilnu primenu u realnim uslovima. Cilj ovog rada je predlog novog postupka za prepoznavanje osoba na osnovu hoda koji bi omogućio razvoj robusnog i pristupačnog biometrijskog sistema. Inicijalno, urađen je sveobuhvatan pregled oblasti i aktuelnih istraživanja na osnovu čega je predložen novi postupak. Predloženi postupak se zasniva na ideji da se sekvenca ljudskog hoda može predstaviti kao jedna nepomična 2D slika. Ovakav postupak omogućio bi da se za potrebe prepoznavanja primene generičke metode za pretragu slika na osnovu sadržaja. Na ovakav način problem bi bio prenet iz prostorno-vremenskog domena u prostorni domen, konkretno domen 2D nepomične slike, koji je poznat i u kome postoji veliki broj dokazanih rešenja. Za potrebe akvizicije, postupak se oslanja na novu tehnologiju iz oblasti interakcije čovek-računar, Microsoft Kinect. Na osnovu predloženog postupka razvijen je modularni laboratorijski prototip kao i okruženje za testiranje i evaluaciju. Naučna zasnovanost i opravdanost predloženog postupka proverena je nizom eksperimenata. Eksperimenti su organizovani na takav način da ispitaju različite faktore koji tokom primene postupka mogu uticati na konačne performanse u prepoznavanju. Na osnovu dobijenih rezultata može se zaključiti da predloženi postupak odlilkuje visok stepen robusnosti kao i visoka preciznost u prepoznavanju...Intense progress of information and communications technology enabled application of biometric technology in identity management. Human gait, as a biometric modality, has great potential for practical application. This is due to its noninvasive and nonintrusive nature. Surveillance technology is especially fertile ground for recognition based on human gait. These facts caused spike in academic interest for this biometric modality. This in turn resulted in development of large number of different approaches to human gait recognition. Nevertheless, practical application of biometric technology based on human gait still trails those well established modalities such as fingerprint, face or voice. Main reason for this is lacking of such approach that would enable stable use in realistic conditions. Goal of this paper is to propose a new approach for human gait recognition that would result in robust and affordable biometric system. Initially, a comprehensive review of research area and existing research was done that served as a base for the proposition of new approach. This new approach is based on the idea that human gait sequence can be represented as a single 2D still image. Using images would open the possibility of applying Content Based Image Retrieval (CBIR) techniques for the purpose of final recognition. This procedure shifts the problem form spatio-temporal towards spatial domain, specifically the space of 2D still image that is well researched and familiar. For acquisition purposes approach relies on new human-computer interaction technology, Microsoft Kinect. As proof of concept, a modular laboratory prototype was developed as well as environment for testing and evaluation. Foundation of the proposed approach was tested through a series of experiments. Empirical evaluation was performed in such a manner to investigate the influence of different contributing factors to system performance. Based on retrieved results a conclusion is reached that the proposed approach is highly robust and achieves high recognition rates..