Single-target tracking of arbitrary objects using multi-layered features and contextual information

This thesis investigated single-target tracking of arbitrary objects. Tracking is a difficult problem due to a variety of challenges such as significant deformations of the target, occlusions, illumination variations, background clutter and camouflage. To achieve robust tracking performance under these severe conditions, this thesis proposed firstly a novel RGB single-target tracker which models the target with multi-layered features and contextual information. The proposed algorithm was tested on two different tracking benchmarks, i.e., VTB and VOT, where it demonstrated significantly more robust performance than other state-of-the-art RGB trackers. Proposed secondly was an extension of the designed RGB tracker to handle RGB-D images using both temporal and spatial constraints to exploit depth information more robustly. For evaluation, the thesis introduced a new RGB-D benchmark dataset with per-frame annotated attributes and extensive bias analysis, on which the proposed tracker achieved the best results. Proposed thirdly was a new tracking approach to handle camouflage problems in highly cluttered scenes exploiting global dynamic constraints from the context. To evaluate the tracker, a benchmark dataset was augmented with a new set of clutter sub-attributes. Using this dataset, it was demonstrated that the proposed method outperforms other state-of-the-art single target trackers on highly cluttered scenes

Multiple Tasks are Better than One: Multi-task Learning and Feature Selection for Head Pose Estimation, Action Recognition and Event Detection

Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images and videos and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information. The classical problem in computer vision is that of determining whether or not the image or video data contains some specific object, feature, or activity. This task can normally be solved robustly and without effort by a human, but is still not satisfactorily solved in computer vision for the general case - arbitrary objects in arbitrary situations. The existing methods for dealing with this problem can at best solve it only for specific objects, such as simple geometric objects (e.g., polyhedra), human faces, printed or hand-written characters, or vehicles, and in specific situations, typically described in terms of well-defined illumination, background, and pose of the object relative to the camera. Machine Learning (ML) and Computer Vision (CV) have been put together during the development of computer vision in the past decade. Nowadays, machine learning is considered as a powerful tool to solve many computer vision problems. Multi-task learning, as one important branch of machine learning, has developed very fast during the past decade. Multi-task learning methods aim to simultaneously learn classification or regression models for a set of related tasks. This typically leads to better models as compared to a learner that does not account for task relationships. The goal of multi-task learning is to improve the performance of learning algorithms by learning classifiers for multiple tasks jointly. This works particularly well if these tasks have some commonality and are generally slightly under-sampled

Consistent visual words mining with adaptive sampling

International audienceState-of-the-art large-scale object retrieval systems usually combine efficient Bag-of-Words indexing models with a spatial verification re-ranking stage to improve query performance. In this paper we propose to directly discover spatially verified visual words as a batch process. Contrary to previous related methods based on feature sets hashing or clustering, we suggest not trading recall for efficiency by sticking on an accurate two-stage matching strategy. The problem then rather becomes a sampling issue: how to effectively and efficiently select relevant query regions while minimizing the number of tentative probes? We therefore introduce an adaptive weighted sampling scheme, starting with some prior distribution and iteratively converging to unvisited regions. Interestingly, the proposed paradigm is generalizable to any input prior distribution, including specific visual concept detectors or efficient hashing-based methods. We show in the experiments that the proposed method allows to discover highly interpretable visual words while providing excellent recall and image representativity

Glocalization in China: An Analysis of Coca-Cola’s Brand Co-Creation Process with Consumers in China

In contemporary marketing, corporations often work to induce consumers to participate in co-creating their brand value. Consumers, therefore, can be considered marketers, who are then used by marketing managers to create competitive advantage and market opportunities. Through processes of co-creation, companies also obtain valuable information about consumer preferences and values, which, in turn, can lower production costs. This thesis uses Coca-Cola as a case study to explore the ways international companies work to incorporate elements of Chinese culture and employ Chinese social media platforms in their promotional messages and activities in order to encourage Chinese consumers to co-create their brand value. The thesis contends that the brand value co-creation process has many implications for Chinese society, including accelerating transformations in Chinese consumers’ cultural values and identities

Data-Efficient Learning of Semantic Segmentation

Semantic segmentation is a fundamental problem in visual perception with a wide range of applications ranging from robotics to autonomous vehicles, and recent approaches based on deep learning have achieved excellent performance. However, to train such systems there is in general a need for very large datasets of annotated images. In this thesis we investigate and propose methods and setups for which it is possible to use unlabelled data to increase the performance or to use limited application specific data to reduce the need for large datasets when learning semantic segmentation.In the first paper we study semantic video segmentation. We present a deep end-to-end trainable model that uses propagated labelling information in unlabelled frames in addition to sparsely labelled frames to predict semantic segmentation. Extensive experiments on the CityScapes and CamVid datasets show that the model can improve accuracy and temporal consistency by using extra unlabelled video frames in training and testing.In the second, third and fourth paper we study active learning for semantic segmentation in an embodied context where navigation is part of the problem. A navigable agent should explore a building and query for the labelling of informative views that increase the visual perception of the agent. In the second paper we introduce the embodied visual active learning problem, and propose and evaluate a range of methods from heuristic baselines to a fully trainable agent using reinforcement learning (RL) on the Matterport3D dataset. We show that the learned agent outperforms several comparable pre-specified baselines. In the third paper we study the embodied visual active learning problem in a lifelong setup, where the visual learning spans the exploration of multiple buildings, and the learning in one scene should influence the active learning in the next e.g. by not annotating already accurately segmented object classes. We introduce new methodology to encourage global exploration of scenes, via an RL-formulation that combines local navigation with global exploration by frontier exploration. We show that the RL-agent can learn adaptable behaviour such as annotating less frequently when it already has explored a number of buildings. Finally we study the embodied visual active learning problem with region-based active learning in the fourth paper. Instead of querying for annotations for a whole image, an agent can query for annotations of just parts of images, and we show that it is significantly more labelling efficient to just annotate regions in the image instead of the full images

Graphic design as urban design: towards a theory for analysing graphic objects in urban environments

This thesis presents a model for analysing the graphic object as urban object, by considering atypical fields of discourse that contribute to the formation of the object domain. The question: what is graphic design as urban design? directs the research through an epistemological design study comprising: an interrogation of graphic design studio practice and the articulation of graphic design research questions; a review and subsequent development of research strategy, design and method towards the articulation of methodology that reflects the nature of the inquiry; a detailed analysis of five different ways to study and research graphic design as urban design, in geography, language, visual communication, art and design, and urban design. The outcome of the investigation is a model that enables future research in the urban environment to benefit from micro-meso-macrographic analysis. The model endeavours to provide a way to evaluate, design and enhance ‘public places and urban spaces’ (Carmona et al., 2010) by considering different scales of symbolic thought and deed. This has been achieved by acknowledging the relationship between the relatively miniscule detail of graphic symbolism, the point at which this becomes visible through increased scale, and the instances when it dominates the urban realm. Examples are considered that show differences between, for example, the size and spacing of letter shapes on a pedestrian sign, compared to the ‘visual’ impact of an iconic building in the cityscape. In between is a myriad of graphic elements that are experienced and designed by many different professional disciplines and occupations. These are evidenced and explained. Throughout the study an indiscriminating literature review is interwoven with the text, accompanied by tabular information, and visual data in the form of photographs and diagrams. This is mainly research-driven data utilising photographs from fieldwork in Brazil, Canada, Hong Kong, Italy, Portugal, South Korea, United Kingdom, and United States of America. The methodology integrates a transdisciplinary adaptive theory approach derived from sociological research, with graphic method (utilising a wider scope of visual data usually associated with graph theory). The following images provide sixteen examples of artefacts representing the graphic object as urban object phenomenon

Black Semiosis: Young Liberian Transnationals Mediating Black Subjectivity and Black Heterogeneity

From the colonization of the “Dark Continent,” to the global industry that turned black bodies into chattel, to the total absence of modern Africa from most American public school curricula, to superfluous representations of African primitivity in mainstream media, to the unflinching state-sanctioned murders of unarmed black people in the Americas, antiblackness and anti-black racism have been part and parcel to modernity, swathing centuries and continents, and seeping into the tiny spaces and moments that constitute social reality for most black-identified human beings. The daily living and theorizing of a small group of twenty-something young people from Liberia provide the marrow of this traditional and virtual ethnographic inquiry into everyday formulations of race via processes of “black semiosis.” As the analytical keynote of the text, black semiosis points us to the processes through which meaning is made about blackness (i.e., how signs are inscribed with racialized meanings and how these signs are deployed on various scales), and it asks that we consider how meaning-making processes and strategies are conditioned by, or made through, blackness (i.e., how the experience of being raced as black codifies ways of making meaning). Specifically, the text uses cultural, linguistic, and semiotic anthropological approaches to examine young transnational Liberians’ productions of verbal and visual “mashups” in face-to-face interactions and online; their theoretical and embodied constructions of gendered and classed models of “sincere” black personhood via hip hop and other globalized phenomena; and their comprehensive semiotic strategies for navigating racialized school structures and discourses in the United States. From their actions, abstractions, and aspirations, I assemble a rendering of black diasporic/transnational subject-formation that yields a keener understanding of the ways black pasts, presents, and futures are currently being made and unmade by a new generation

Cognitive and Affective Evaluation in Forming Unique Destination Image Among Tourists Visiting Malacca

Since Melaka is positioned as Historical City inaugurated by UNESCO in 2008, the study suggests unique image as a new component of image associations. A number of overseas tourists were selected as samples . Results showed that unique image was significantly constructed and affected by cognitive and affective evaluations. Cognitive evaluation was significantly affected by the types of information source, while affective evaluation was affected significantly by social psychological motivations. The research proves that Melaka has fulfilled the requirements to differentiate the city as a unique tourist destination. The positioning of Melaka as truly Malaysia and World heritage should be translated into the rational benefit of encountering unspoiled historical side and multi-racial living cultures. Positive unique image creation leads to intention to revisit and recommend others experiencing the world heritage and history of Melaka