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

Visualisation of the information resources for cell biology

Intelligent multimodal interfaces can facilitate scientists in utilising available information resources. Combining scientific visualisations with interactive and intelligent tools can help create a “habitable” information space. Development of such tools remains largely iterative. We discuss an ongoing implementation of intelligent interactive visualisation of information resources in cell biology

Making sense of group interaction in an ambient intelligent environment for physical play

This paper presents the results of a study on group interaction with a prototype known as socio-ec(h)o. socioec(h)o explores the design of sensing and display, user modeling, and interaction in an embedded interaction system utilizing a game structure. Our study involved the playing of our prototype system by thirty-six (36) participants grouped into teams of four (4). Our aim was to determine heuristics that we could use to further design the interaction and user model approaches for group and embodied interaction systems. We analyzed group interaction and performance based on factors of team cohesion and goal focus. We found that with our system, these factors alone could not explain performance. However, when transitions in the degrees of each factor, i.e. high, medium or low are considered, a clearer picture for performance emerges. The significance of the results is that they describe recognizable factors for positive group interaction

Towards modelling group-robot interactions using a qualitative spatial representation

This paper tackles the problem of finding a suitable qualitative representation for robots to reason about activity spaces where they carry out tasks interacting with a group of people. The Qualitative Spatial model for Group Robot Interaction (QS-GRI) defines Kendon-formations depending on: (i) the relative location of the robot with respect to other individuals involved in that interaction; (ii) the individuals' orientation; (iii) the shared peri-personal distance; and (iv) the role of the individuals (observer, main character or interactive). The evolution of Kendon-formations between is studied, that is, how one formation is transformed into another. These transformations can depend on the role that the robot have, and on the amount of people involved.Postprint (author's final draft

A Linear General Type-2 Fuzzy Logic Based Computing With Words Approach for Realising an Ambient Intelligent Platform for Cooking Recipes Recommendation

This paper addresses the need to enhance transparency in ambient intelligent environments by developing more natural ways of interaction, which allow the users to communicate easily with the hidden networked devices rather than embedding obtrusive tablets and computing equipment throughout their surroundings. Ambient intelligence vision aims to realize digital environments that adapt to users in a responsive, transparent, and context-aware manner in order to enhance users' comfort. It is, therefore, appropriate to employ the paradigm of “computing with words” (CWWs), which aims to mimic the ability of humans to communicate transparently and manipulate perceptions via words. One of the daily activities that would increase the comfort levels of the users (especially people with disabilities) is cooking and performing tasks in the kitchen. Existing approaches on food preparation, cooking, and recipe recommendation stress on healthy eating and balanced meal choices while providing limited personalization features through the use of intrusive user interfaces. Herein, we present an application, which transparently interacts with users based on a novel CWWs approach in order to predict the recipe's difficulty level and to recommend an appropriate recipe depending on the user's mood, appetite, and spare time. The proposed CWWs framework is based on linear general type-2 (LGT2) fuzzy sets, which linearly quantify the linguistic modifiers in the third dimension in order to better represent the user perceptions while avoiding the drawbacks of type-1 and interval type-2 fuzzy sets. The LGT2-based CWWs framework can learn from user experiences and adapt to them in order to establish more natural human-machine interaction. We have carried numerous real-world experiments with various users in the University of Essex intelligent flat. The comparison analysis between interval type-2 fuzzy sets and LGT2 fuzzy sets demonstrates up to 55.43% improvement when general type-2 fuzzy sets are used than when interval type-2 fuzzy sets are used instead. The quantitative and qualitative analysis both show the success of the system in providing a natural interaction with the users for recommending food recipes where the quantitative analysis shows the high statistical correlation between the system output and the users' feedback; the qualitative analysis presents social scienc

Assistive robotics: research challenges and ethics education initiatives

Assistive robotics is a fast growing field aimed at helping healthcarers in hospitals, rehabilitation centers and nursery homes, as well as empowering people with reduced mobility at home, so that they can autonomously fulfill their daily living activities. The need to function in dynamic human-centered environments poses new research challenges: robotic assistants need to have friendly interfaces, be highly adaptable and customizable, very compliant and intrinsically safe to people, as well as able to handle deformable materials. Besides technical challenges, assistive robotics raises also ethical defies, which have led to the emergence of a new discipline: Roboethics. Several institutions are developing regulations and standards, and many ethics education initiatives include contents on human-robot interaction and human dignity in assistive situations. In this paper, the state of the art in assistive robotics is briefly reviewed, and educational materials from a university course on Ethics in Social Robotics and AI focusing on the assistive context are presented.Peer ReviewedPostprint (author's final draft

Feature-based image patch classification for moving shadow detection

Moving object detection is a first step towards many computer vision applications, such as human interaction and tracking, video surveillance, and traffic monitoring systems. Accurate estimation of the target object’s size and shape is often required before higher-level tasks (e.g., object tracking or recog nition) can be performed. However, these properties can be derived only when the foreground object is detected precisely. Background subtraction is a common technique to extract foreground objects from image sequences. The purpose of background subtraction is to detect changes in pixel values within a given frame. The main problem with background subtraction and other related object detection techniques is that cast shadows tend to be misclassified as either parts of the foreground objects (if objects and their cast shadows are bonded together) or independent foreground objects (if objects and shadows are separated). The reason for this phenomenon is the presence of similar characteristics between the target object and its cast shadow, i.e., shadows have similar motion, attitude, and intensity changes as the moving objects that cast them. Detecting shadows of moving objects is challenging because of problem atic situations related to shadows, for example, chromatic shadows, shadow color blending, foreground-background camouflage, nontextured surfaces and dark surfaces. Various methods for shadow detection have been proposed in the liter ature to address these problems. Many of these methods use general-purpose image feature descriptors to detect shadows. These feature descriptors may be effective in distinguishing shadow points from the foreground object in a specific problematic situation; however, such methods often fail to distinguish shadow points from the foreground object in other situations. In addition, many of these moving shadow detection methods require prior knowledge of the scene condi tions and/or impose strong assumptions, which make them excessively restrictive in practice. The aim of this research is to develop an efficient method capable of addressing possible environmental problems associated with shadow detection while simultaneously improving the overall accuracy and detection stability. In this research study, possible problematic situations for dynamic shad ows are addressed and discussed in detail. On the basis of the analysis, a ro bust method, including change detection and shadow detection, is proposed to address these environmental problems. A new set of two local feature descrip tors, namely, binary patterns of local color constancy (BPLCC) and light-based gradient orientation (LGO), is introduced to address the identified problematic situations by incorporating intensity, color, texture, and gradient information. The feature vectors are concatenated in a column-by-column manner to con struct one dictionary for the objects and another dictionary for the shadows. A new sparse representation framework is then applied to find the nearest neighbor of the test image segment by computing a weighted linear combination of the reference dictionary. Image segment classification is then performed based on the similarity between the test image and the sparse representations of the two classes. The performance of the proposed framework on common shadow detec tion datasets is evaluated, and the method shows improved performance com pared with state-of-the-art methods in terms of the shadow detection rate, dis crimination rate, accuracy, and stability. By achieving these significant improve ments, the proposed method demonstrates its ability to handle various problems associated with image processing and accomplishes the aim of this thesis