Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Advances in Monocular Exemplar-based Human Body Pose Analysis: Modeling, Detection and Tracking

Esta tesis contribuye en el análisis de la postura del cuerpo humano a partir de secuencias de imágenes adquiridas con una sola cámara. Esta temática presenta un amplio rango de potenciales aplicaciones en video-vigilancia, video-juegos o aplicaciones biomédicas. Las técnicas basadas en patrones han tenido éxito, sin embargo, su precisión depende de la similitud del punto de vista de la cámara y de las propiedades de la escena entre las imágenes de entrenamiento y las de prueba. Teniendo en cuenta un conjunto de datos de entrenamiento capturado mediante un número reducido de cámaras fijas, paralelas al suelo, se han identificado y analizado tres escenarios posibles con creciente nivel de dificultad: 1) una cámara estática paralela al suelo, 2) una cámara de vigilancia fija con un ángulo de visión considerablemente diferente, y 3) una secuencia de video capturada con una cámara en movimiento o simplemente una sola imagen estática

Methods for Analysing Endothelial Cell Shape and Behaviour in Relation to the Focal Nature of Atherosclerosis

The aim of this thesis is to develop automated methods for the analysis of the spatial patterns, and the functional behaviour of endothelial cells, viewed under microscopy, with applications to the understanding of atherosclerosis. Initially, a radial search approach to segmentation was attempted in order to trace the cell and nuclei boundaries using a maximum likelihood algorithm; it was found inadequate to detect the weak cell boundaries present in the available data. A parametric cell shape model was then introduced to fit an equivalent ellipse to the cell boundary by matching phase-invariant orientation fields of the image and a candidate cell shape. This approach succeeded on good quality images, but failed on images with weak cell boundaries. Finally, a support vector machines based method, relying on a rich set of visual features, and a small but high quality training dataset, was found to work well on large numbers of cells even in the presence of strong intensity variations and imaging noise. Using the segmentation results, several standard shear-stress dependent parameters of cell morphology were studied, and evidence for similar behaviour in some cell shape parameters was obtained in in-vivo cells and their nuclei. Nuclear and cell orientations around immature and mature aortas were broadly similar, suggesting that the pattern of flow direction near the wall stayed approximately constant with age. The relation was less strong for the cell and nuclear length-to-width ratios. Two novel shape analysis approaches were attempted to find other properties of cell shape which could be used to annotate or characterise patterns, since a wide variability in cell and nuclear shapes was observed which did not appear to fit the standard parameterisations. Although no firm conclusions can yet be drawn, the work lays the foundation for future studies of cell morphology. To draw inferences about patterns in the functional response of cells to flow, which may play a role in the progression of disease, single-cell analysis was performed using calcium sensitive florescence probes. Calcium transient rates were found to change with flow, but more importantly, local patterns of synchronisation in multi-cellular groups were discernable and appear to change with flow. The patterns suggest a new functional mechanism in flow-mediation of cell-cell calcium signalling

Review of Person Re-identification Techniques

Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201

Robust gait recognition under variable covariate conditions

PhDGait is a weak biometric when compared to face, fingerprint or iris because it can be easily affected by various conditions. These are known as the covariate conditions and include clothing, carrying, speed, shoes and view among others. In the presence of variable covariate conditions gait recognition is a hard problem yet to be solved with no working system reported. In this thesis, a novel gait representation, the Gait Flow Image (GFI), is proposed to extract more discriminative information from a gait sequence. GFI extracts the relative motion of body parts in different directions in separate motion descriptors. Compared to the existing model-free gait representations, GFI is more discriminative and robust to changes in covariate conditions. In this thesis, gait recognition approaches are evaluated without the assumption on cooperative subjects, i.e. both the gallery and the probe sets consist of gait sequences under different and unknown covariate conditions. The results indicate that the performance of the existing approaches drops drastically under this more realistic set-up. It is argued that selecting the gait features which are invariant to changes in covariate conditions is the key to developing a gait recognition system without subject cooperation. To this end, the Gait Entropy Image (GEnI) is proposed to perform automatic feature selection on each pair of gallery and probe gait sequences. Moreover, an Adaptive Component and Discriminant Analysis is formulated which seamlessly integrates the feature selection method with subspace analysis for fast and robust recognition. Among various factors that affect the performance of gait recognition, change in viewpoint poses the biggest problem and is treated separately. A novel approach to address this problem is proposed in this thesis by using Gait Flow Image in a cross view gait recognition framework with the view angle of a probe gait sequence unknown. A Gaussian Process classification technique is formulated to estimate the view angle of each probe gait sequence. To measure the similarity of gait sequences across view angles, the correlation of gait sequences from different views is modelled using Canonical Correlation Analysis and the correlation strength is used as a similarity measure. This differs from existing approaches, which reconstruct gait features in different views through 2D view transformation or 3D calibration. Without explicit reconstruction, the proposed method can cope with feature mis-match across view and is more robust against feature noise

Gait recognition in the wild using shadow silhouettes

Gait recognition systems allow identification of users relying on features acquired from their body movement while walking. This paper discusses the main factors affecting the gait features that can be acquired from a 2D video sequence, proposing a taxonomy to classify them across four dimensions. It also explores the possibility of obtaining users’ gait features from the shadow silhouettes by proposing a novel gait recognition system. The system includes novel methods for: (i) shadow segmentation, (ii) walking direction identification, and (iii) shadow silhouette rectification. The shadow segmentation is performed by fitting a line through the feet positions of the user obtained from the gait texture image (GTI). The direction of the fitted line is then used to identify the walking direction of the user. Finally, the shadow silhouettes thus obtained are rectified to compensate for the distortions and deformations resulting from the acquisition setup, using the proposed four-point correspondence method. The paper additionally presents a new database, consisting of 21 users moving along two walking directions, to test the proposed gait recognition system. Results show that the performance of the proposed system is equivalent to that of the state-of-the-art in a constrained setting, but performing equivalently well in the wild, where most state-of-the-art methods fail. The results also highlight the advantages of using rectified shadow silhouettes over body silhouettes under certain conditions.info:eu-repo/semantics/acceptedVersio

Video content analysis for automated detection and tracking of humans in CCTV surveillance applications

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented

Automatic annotation for weakly supervised learning of detectors

PhDObject detection in images and action detection in videos are among the most widely studied computer vision problems, with applications in consumer photography, surveillance, and automatic media tagging. Typically, these standard detectors are fully supervised, that is they require a large body of training data where the locations of the objects/actions in images/videos have been manually annotated. With the emergence of digital media, and the rise of high-speed internet, raw images and video are available for little to no cost. However, the manual annotation of object and action locations remains tedious, slow, and expensive. As a result there has been a great interest in training detectors with weak supervision where only the presence or absence of object/action in image/video is needed, not the location. This thesis presents approaches for weakly supervised learning of object/action detectors with a focus on automatically annotating object and action locations in images/videos using only binary weak labels indicating the presence or absence of object/action in images/videos. First, a framework for weakly supervised learning of object detectors in images is presented. In the proposed approach, a variation of multiple instance learning (MIL) technique for automatically annotating object locations in weakly labelled data is presented which, unlike existing approaches, uses inter-class and intra-class cue fusion to obtain the initial annotation. The initial annotation is then used to start an iterative process in which standard object detectors are used to refine the location annotation. Finally, to ensure that the iterative training of detectors do not drift from the object of interest, a scheme for detecting model drift is also presented. Furthermore, unlike most other methods, our weakly supervised approach is evaluated on data without manual pose (object orientation) annotation. Second, an analysis of the initial annotation of objects, using inter-class and intra-class cues, is carried out. From the analysis, a new method based on negative mining (NegMine) is presented for the initial annotation of both object and action data. The NegMine based approach is a much simpler formulation using only inter-class measure and requires no complex combinatorial optimisation but can still meet or outperform existing approaches including the previously pre3 sented inter-intra class cue fusion approach. Furthermore, NegMine can be fused with existing approaches to boost their performance. Finally, the thesis will take a step back and look at the use of generic object detectors as prior knowledge in weakly supervised learning of object detectors. These generic object detectors are typically based on sampling saliency maps that indicate if a pixel belongs to the background or foreground. A new approach to generating saliency maps is presented that, unlike existing approaches, looks beyond the current image of interest and into images similar to the current image. We show that our generic object proposal method can be used by itself to annotate the weakly labelled object data with surprisingly high accuracy

Human shape modelling for carried object detection and segmentation

La détection des objets transportés est un des prérequis pour développer des systèmes qui cherchent à comprendre les activités impliquant des personnes et des objets. Cette thèse présente de nouvelles méthodes pour détecter et segmenter les objets transportés dans des vidéos de surveillance. Les contributions sont divisées en trois principaux chapitres. Dans le premier chapitre, nous introduisons notre détecteur d’objets transportés, qui nous permet de détecter un type générique d’objets. Nous formulons la détection d’objets transportés comme un problème de classification de contours. Nous classifions le contour des objets mobiles en deux classes : objets transportés et personnes. Un masque de probabilités est généré pour le contour d’une personne basé sur un ensemble d’exemplaires (ECE) de personnes qui marchent ou se tiennent debout de différents points de vue. Les contours qui ne correspondent pas au masque de probabilités généré sont considérés comme des candidats pour être des objets transportés. Ensuite, une région est assignée à chaque objet transporté en utilisant la Coupe Biaisée Normalisée (BNC) avec une probabilité obtenue par une fonction pondérée de son chevauchement avec l’hypothèse du masque de contours de la personne et du premier plan segmenté. Finalement, les objets transportés sont détectés en appliquant une Suppression des Non-Maxima (NMS) qui élimine les scores trop bas pour les objets candidats. Le deuxième chapitre de contribution présente une approche pour détecter des objets transportés avec une méthode innovatrice pour extraire des caractéristiques des régions d’avant-plan basée sur leurs contours locaux et l’information des super-pixels. Initiallement, un objet bougeant dans une séquence vidéo est segmente en super-pixels sous plusieurs échelles. Ensuite, les régions ressemblant à des personnes dans l’avant-plan sont identifiées en utilisant un ensemble de caractéristiques extraites de super-pixels dans un codebook de formes locales. Ici, les régions ressemblant à des humains sont équivalentes au masque de probabilités de la première méthode (ECE). Notre deuxième détecteur d’objets transportés bénéficie du nouveau descripteur de caractéristiques pour produire une carte de probabilité plus précise. Les compléments des super-pixels correspondants aux régions ressemblant à des personnes dans l’avant-plan sont considérés comme une carte de probabilité des objets transportés. Finalement, chaque groupe de super-pixels voisins avec une haute probabilité d’objets transportés et qui ont un fort support de bordure sont fusionnés pour former un objet transporté. Finalement, dans le troisième chapitre, nous présentons une méthode pour détecter et segmenter les objets transportés. La méthode proposée adopte le nouveau descripteur basé sur les super-pixels pour iii identifier les régions ressemblant à des objets transportés en utilisant la modélisation de la forme humaine. En utilisant l’information spatio-temporelle des régions candidates, la consistance des objets transportés récurrents, vus dans le temps, est obtenue et sert à détecter les objets transportés. Enfin, les régions d’objets transportés sont raffinées en intégrant de l’information sur leur apparence et leur position à travers le temps avec une extension spatio-temporelle de GrabCut. Cette étape finale sert à segmenter avec précision les objets transportés dans les séquences vidéo. Nos méthodes sont complètement automatiques, et font des suppositions minimales sur les personnes, les objets transportés, et les les séquences vidéo. Nous évaluons les méthodes décrites en utilisant deux ensembles de données, PETS 2006 et i-Lids AVSS. Nous évaluons notre détecteur et nos méthodes de segmentation en les comparant avec l’état de l’art. L’évaluation expérimentale sur les deux ensembles de données démontre que notre détecteur d’objets transportés et nos méthodes de segmentation surpassent de façon significative les algorithmes compétiteurs.Detecting carried objects is one of the requirements for developing systems that reason about activities involving people and objects. This thesis presents novel methods to detect and segment carried objects in surveillance videos. The contributions are divided into three main chapters. In the first, we introduce our carried object detector which allows to detect a generic class of objects. We formulate carried object detection in terms of a contour classification problem. We classify moving object contours into two classes: carried object and person. A probability mask for person’s contours is generated based on an ensemble of contour exemplars (ECE) of walking/standing humans in different viewing directions. Contours that are not falling in the generated hypothesis mask are considered as candidates for carried object contours. Then, a region is assigned to each carried object candidate contour using Biased Normalized Cut (BNC) with a probability obtained by a weighted function of its overlap with the person’s contour hypothesis mask and segmented foreground. Finally, carried objects are detected by applying a Non-Maximum Suppression (NMS) method which eliminates the low score carried object candidates. The second contribution presents an approach to detect carried objects with an innovative method for extracting features from foreground regions based on their local contours and superpixel information. Initially, a moving object in a video frame is segmented into multi-scale superpixels. Then human-like regions in the foreground area are identified by matching a set of extracted features from superpixels against a codebook of local shapes. Here the definition of human like regions is equivalent to a person’s probability map in our first proposed method (ECE). Our second carried object detector benefits from the novel feature descriptor to produce a more accurate probability map. Complement of the matching probabilities of superpixels to human-like regions in the foreground are considered as a carried object probability map. At the end, each group of neighboring superpixels with a high carried object probability which has strong edge support is merged to form a carried object. Finally, in the third contribution we present a method to detect and segment carried objects. The proposed method adopts the new superpixel-based descriptor to identify carried object-like candidate regions using human shape modeling. Using spatio-temporal information of the candidate regions, consistency of recurring carried object candidates viewed over time is obtained and serves to detect carried objects. Last, the detected carried object regions are refined by integrating information of their appearances and their locations over time with a spatio-temporal extension of GrabCut. This final stage is used to accurately segment carried objects in frames. Our methods are fully automatic, and make minimal assumptions about a person, carried objects and videos. We evaluate the aforementioned methods using two available datasets PETS 2006 and i-Lids AVSS. We compare our detector and segmentation methods against a state-of-the-art detector. Experimental evaluation on the two datasets demonstrates that both our carried object detection and segmentation methods significantly outperform competing algorithms