Human body analysis using depth data

Human body analysis is one of the broadest areas within the computer vision field. Researchers have put a strong effort in the human body analysis area, specially over the last decade, due to the technological improvements in both video cameras and processing power. Human body analysis covers topics such as person detection and segmentation, human motion tracking or action and behavior recognition. Even if human beings perform all these tasks naturally, they build-up a challenging problem from a computer vision point of view. Adverse situations such as viewing perspective, clutter and occlusions, lighting conditions or variability of behavior amongst persons may turn human body analysis into an arduous task. In the computer vision field, the evolution of research works is usually tightly related to the technological progress of camera sensors and computer processing power. Traditional human body analysis methods are based on color cameras. Thus, the information is extracted from the raw color data, strongly limiting the proposals. An interesting quality leap was achieved by introducing the multiview concept. That is to say, having multiple color cameras recording a single scene at the same time. With multiview approaches, 3D information is available by means of stereo matching algorithms. The fact of having 3D information is a key aspect in human motion analysis, since the human body moves in a three-dimensional space. Thus, problems such as occlusion and clutter may be overcome with 3D information. The appearance of commercial depth cameras has supposed a second leap in the human body analysis field. While traditional multiview approaches required a cumbersome and expensive setup, as well as a fine camera calibration; novel depth cameras directly provide 3D information with a single camera sensor. Furthermore, depth cameras may be rapidly installed in a wide range of situations, enlarging the range of applications with respect to multiview approaches. Moreover, since depth cameras are based on infra-red light, they do not suffer from illumination variations. In this thesis, we focus on the study of depth data applied to the human body analysis problem. We propose novel ways of describing depth data through specific descriptors, so that they emphasize helpful characteristics of the scene for further body analysis. These descriptors exploit the special 3D structure of depth data to outperform generalist 3D descriptors or color based ones. We also study the problem of person detection, proposing a highly robust and fast method to detect heads. Such method is extended to a hand tracker, which is used throughout the thesis as a helpful tool to enable further research. In the remainder of this dissertation, we focus on the hand analysis problem as a subarea of human body analysis. Given the recent appearance of depth cameras, there is a lack of public datasets. We contribute with a dataset for hand gesture recognition and fingertip localization using depth data. This dataset acts as a starting point of two proposals for hand gesture recognition and fingertip localization based on classification techniques. In these methods, we also exploit the above mentioned descriptor proposals to finely adapt to the nature of depth data.%, and enhance the results in front of traditional color-based methods.L’anàlisi del cos humà és una de les àrees més àmplies del camp de la visió per computador. Els investigadors han posat un gran esforç en el camp de l’anàlisi del cos humà, sobretot durant la darrera dècada, degut als grans avenços tecnològics, tant pel que fa a les càmeres com a la potencia de càlcul. L’anàlisi del cos humà engloba varis temes com la detecció i segmentació de persones, el seguiment del moviment del cos, o el reconeixement d'accions. Tot i que els essers humans duen a terme aquestes tasques d'una manera natural, es converteixen en un difícil problema quan s'ataca des de l’òptica de la visió per computador. Situacions adverses, com poden ser la perspectiva del punt de vista, les oclusions, les condicions d’il•luminació o la variabilitat de comportament entre persones, converteixen l’anàlisi del cos humà en una tasca complicada. En el camp de la visió per computador, l’evolució de la recerca va sovint lligada al progrés tecnològic, tant dels sensors com de la potencia de càlcul dels ordinadors. Els mètodes tradicionals d’anàlisi del cos humà estan basats en càmeres de color. Això limita molt els enfocaments, ja que la informació disponible prové únicament de les dades de color. El concepte multivista va suposar salt de qualitat important. En els enfocaments multivista es tenen múltiples càmeres gravant una mateixa escena simultàniament, permetent utilitzar informació 3D gràcies a algorismes de combinació estèreo. El fet de disposar d’informació 3D es un punt clau, ja que el cos humà es mou en un espai tri-dimensional. Això doncs, problemes com les oclusions es poden apaivagar si es disposa de informació 3D. L’aparició de les càmeres de profunditat comercials ha suposat un segon salt en el camp de l’anàlisi del cos humà. Mentre els mètodes multivista tradicionals requereixen un muntatge pesat i car, i una celebració precisa de totes les càmeres; les noves càmeres de profunditat ofereixen informació 3D de forma directa amb un sol sensor. Aquestes càmeres es poden instal•lar ràpidament en una gran varietat d'entorns, ampliant enormement l'espectre d'aplicacions, que era molt reduït amb enfocaments multivista. A més a més, com que les càmeres de profunditat estan basades en llum infraroja, no pateixen problemes relacionats amb canvis d’il•luminació. En aquesta tesi, ens centrem en l'estudi de la informació que ofereixen les càmeres de profunditat, i la seva aplicació al problema d’anàlisi del cos humà. Proposem noves vies per descriure les dades de profunditat mitjançant descriptors específics, capaços d'emfatitzar característiques de l'escena que seran útils de cara a una posterior anàlisi del cos humà. Aquests descriptors exploten l'estructura 3D de les dades de profunditat per superar descriptors 3D generalistes o basats en color. També estudiem el problema de detecció de persones, proposant un mètode per detectar caps robust i ràpid. Ampliem aquest mètode per obtenir un algorisme de seguiment de mans que ha estat utilitzat al llarg de la tesi. En la part final del document, ens centrem en l’anàlisi de les mans com a subàrea de l’anàlisi del cos humà. Degut a la recent aparició de les càmeres de profunditat, hi ha una manca de bases de dades públiques. Contribuïm amb una base de dades pensada per la localització de dits i el reconeixement de gestos utilitzant dades de profunditat. Aquesta base de dades és el punt de partida de dues contribucions sobre localització de dits i reconeixement de gestos basades en tècniques de classificació. En aquests mètodes, també explotem les ja mencionades propostes de descriptors per millor adaptar-nos a la naturalesa de les dades de profunditat

Reinterpreting EU Air Transport Deregulation: A Disaggregated Analysis of the Spatial Distribution of Traffic in Europe, 1990-2009

This paper analyses the spatial distribution of seat capacity in the EU from 1990 to 2009 and sheds light on the contrasting results in the literature. It contributes to the debate on the deregulation and whether the rise of hub-and-spoke networks and the success of low-cost carriers lead to concentration or deconcentration. We use the Gini index and its decomposition to evaluate the contribution of airport subgroups and airline networks to the overall concentration of seat capacity. We conclude that, overall, seat capacity follows a spatial deconcentration pattern. While intra-EU seat capacity became more spatially deconcentrated, extra-EU seat capacity concentrated. However, our results do not support the general view that network carriers tend to increase concentration levels and low-cost carriers to decrease them, leading us to a reinterpretation of the impacts of air transport deregulation. The results show the increasing importance of foreign carriers and new strategies such as hub-bypassing

The evolving low-cost business model: Network implications of fare bundling and connecting flights in Europe

In a context of limited organic growth, some low-cost airlines have considered business strategies that are changing two key principles of the low-cost airline business model: fare unbundling and point-to-point operations. Using a multivariate analysis we identify the influence of several route characteristics on the share that European pure low-cost and hybrid low-cost carriers have on the routes they operate. Results show that, from a network perspective, the distance between the archetypical low-cost carrier business model and the adapted low-cost carrier business model with a hybrid approach is widening. Differences are also clear between hybrids offering connecting services and hybrids offering fare bundling. The results are also important from an airport policy perspective, since secondary airports and legacy airports in transition could be able to reduce the gap between them and the main hub airports

Resultats de les darreres campanyes d'excavació a l'establiment ibèric i la vil·la romana de Darró (Vilanova i la Geltrú, Garraf)

Entre 1996 i 1998, s'han dut a terme nombrosos treballs a Darró. En aquesta comunicació presentem els que han afectat la zona O del jaciment, situada a ponent; en el decurs dels quals s'han estudiat bàsicament dues cases del poblat ibèric, que van funcionar entre el començament del segle 11 i mitjan segle 1 aC (les núm. 2 i 3). També s'han analitzat algunes estructures precedents, dels segles IV-111 aC, així com l'evolució d'aquesta àrea en època imperial romana, entre mitjan segle 1 i mitjan segle IV, quan hi va haver dependències de la pars rústica de la vil·la. Tot seguit, va ser utilitzada per situar-hi una necròpoli tardan

DeepPCR: Parallelizing Sequential Operations in Neural Networks

Parallelization techniques have become ubiquitous for accelerating inference and training of deep neural networks. Despite this, several operations are still performed in a sequential manner. For instance, the forward and backward passes are executed layer-by-layer, and the output of diffusion models is produced by applying a sequence of denoising steps. This sequential approach results in a computational cost proportional to the number of steps involved, presenting a potential bottleneck as the number of steps increases. In this work, we introduce DeepPCR, a novel algorithm which parallelizes typically sequential operations in order to speed up inference and training of neural networks. DeepPCR is based on interpreting a sequence of $L$ steps as the solution of a specific system of equations, which we recover using the Parallel Cyclic Reduction algorithm. This reduces the complexity of computing the sequential operations from $\mathcal{O}(L)$ to $\mathcal{O}(\log_2L)$, thus yielding a speedup for large $L$. To verify the theoretical lower complexity of the algorithm, and to identify regimes for speedup, we test the effectiveness of DeepPCR in parallelizing the forward and backward pass in multi-layer perceptrons, and reach speedups of up to $30\times$ for the forward and $200\times$ for the backward pass. We additionally showcase the flexibility of DeepPCR by parallelizing training of ResNets with as many as 1024 layers, and generation in diffusion models, enabling up to $7\times$ faster training and $11\times$ faster generation, respectively, when compared to the sequential approach

Designing Data: Proactive Data Collection and Iteration for Machine Learning

Lack of diversity in data collection has caused significant failures in machine learning (ML) applications. While ML developers perform post-collection interventions, these are time intensive and rarely comprehensive. Thus, new methods to track and manage data collection, iteration, and model training are necessary for evaluating whether datasets reflect real world variability. We present designing data, an iterative, bias mitigating approach to data collection connecting HCI concepts with ML techniques. Our process includes (1) Pre-Collection Planning, to reflexively prompt and document expected data distributions; (2) Collection Monitoring, to systematically encourage sampling diversity; and (3) Data Familiarity, to identify samples that are unfamiliar to a model through Out-of-Distribution (OOD) methods. We instantiate designing data through our own data collection and applied ML case study. We find models trained on "designed" datasets generalize better across intersectional groups than those trained on similarly sized but less targeted datasets, and that data familiarity is effective for debugging datasets

Differential affinity of mammalian histone H1 somatic subtypes for DNA and chromatin

<p>Abstract</p> <p>Background</p> <p>Histone H1 is involved in the formation and maintenance of chromatin higher order structure. H1 has multiple isoforms; the subtypes differ in timing of expression, extent of phosphorylation and turnover rate. In vertebrates, the amino acid substitution rates differ among subtypes by almost one order of magnitude, suggesting that each subtype might have acquired a unique function. We have devised a competitive assay to estimate the relative binding affinities of histone H1 mammalian somatic subtypes H1a-e and H1° for long chromatin fragments (30–35 nucleosomes) in physiological salt (0.14 M NaCl) at constant stoichiometry.</p> <p>Results</p> <p>The H1 complement of native chromatin was perturbed by adding an additional amount of one of the subtypes. A certain amount of SAR (scaffold-associated region) DNA was present in the mixture to avoid precipitation of chromatin by excess H1. SAR DNA also provided a set of reference relative affinities, which were needed to estimate the relative affinities of the subtypes for chromatin from the distribution of the subtypes between the SAR and the chromatin. The amounts of chromatin, SAR and additional H1 were adjusted so as to keep the stoichiometry of perturbed chromatin similar to that of native chromatin. H1 molecules freely exchanged between the chromatin and SAR binding sites. In conditions of free exchange, H1a was the subtype of lowest affinity, H1b and H1c had intermediate affinities and H1d, H1e and H1° the highest affinities. Subtype affinities for chromatin differed by up to 19-fold. The relative affinities of the subtypes for chromatin were equivalent to those estimated for a SAR DNA fragment and a pUC19 fragment of similar length. Avian H5 had an affinity ~12-fold higher than H1e for both DNA and chromatin.</p> <p>Conclusion</p> <p>H1 subtypes freely exchange <it>in vitro </it>between chromatin binding sites in physiological salt (0.14 M NaCl). The large differences in relative affinity of the H1 subtypes for chromatin suggest that differential affinity could be functionally relevant and thus contribute to the functional differentiation of the subtypes. The conservation of the relative affinities for SAR and non-SAR DNA, in spite of a strong preference for SAR sequences, indicates that differential affinity alone cannot be responsible for the heterogeneous distribution of some subtypes in cell nuclei.</p

Collaborative voting of 3D features for robust gesture estimation

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Human body analysis raises special interest because it enables a wide range of interactive applications. In this paper we present a gesture estimator that discriminates body poses in depth images. A novel collaborative method is proposed to learn 3D features of the human body and, later, to estimate specific gestures. The collaborative estimation framework is inspired by decision forests, where each selected point (anchor point) contributes to the estimation by casting votes. The main idea is to detect a body part by accumulating the inference of other trained body parts. The collaborative voting encodes the global context of human pose, while 3D features represent local appearance. Body parts contributing to the detection are interpreted as a voting process. Experimental results for different 3D features prove the validity of the proposed algorithm.Peer ReviewedPostprint (author's final draft