Kinect and Episodic Reasoning for Human Action Recognition

This paper presents a method for rational behaviour recognition that combines vision-based pose estimation with knowledge modeling and reasoning. The proposed method consists of two stages. First, RGB-D images are used in the estimation of the body postures. Then, estimated actions are evaluated to verify that they make sense. This method requires rational behaviour to be exhibited. To comply with this requirement, this work proposes a rational RGB-D dataset with two types of sequences, some for training and some for testing. Preliminary results show the addition of knowledge modeling and reasoning leads to a significant increase of recognition accuracy when compared to a system based only on computer vision.

ReadingAct RGB-D action dataset and human action recognition from local features

For general home monitoring, a system should automatically interpret people’s actions. The system should be non-intrusive, and able to deal with a cluttered background, and loose clothes. An approach based on spatio-temporal local features and a Bag-of-Words (BoW) model is proposed for single-person action recognition from combined intensity and depth images. To restore the temporal structure lost in the traditional BoW method, a dynamic time alignment technique with temporal binning is applied in this work, which has not been previously implemented in the literature for human action recognition on depth imagery. A novel human action dataset with depth data has been created using two Microsoft Kinect sensors. The ReadingAct dataset contains 20 subjects and 19 actions for a total of 2340 videos. To investigate the effect of using depth images and the proposed method, testing was conducted on three depth datasets, and the proposed method was compared to traditional Bag-of-Words methods. Results showed that the proposed method improves recognition accuracy when adding depth to the conventional intensity data, and has advantages when dealing with long actions.

A framework to understand human action

O trabalho consiste numa análise de dois modelos explicativos do comportamento humano considerados fundamentais na literatura contemporânea sobre teoria da ação. O primeiro modelo, o causalista, tenta explicar as ações em termos de causas e leis gerais. O segundo explica a ação em termos de intenções e silogismos práticos. As dificuldades e problemas de ambos modelos são apresentadas e na última parte do ensaio propomos um modelo alternativo baseado na noção de retrodução.

Image summarisation: human action description from static images

Dissertação de Mestrado, Processamento de Linguagem Natural e Indústrias da Língua, Faculdade de Ciências Humanas e Sociais, Universidade do Algarve, 2014

Adaptive unsupervised learning of human actions

Automatic detection of suspicious activities in CCTV camera feeds is crucial to the success of video surveillance systems. Such a capability can help transform the dumb CCTV cameras into smart surveillance tools for fighting crime and terror. Learning and classification of basic human actions is a precursor to detecting suspicious activities. Most of the current approaches rely on a non-realistic assumption that a complete dataset of normal human actions is available. This paper presents a different approach to deal with the problem of understanding human actions in video when no prior information is available. This is achieved by working with an incomplete dataset of basic actions which are continuously updated. Initially, all video segments are represented by Bags-Of-Words (BOW) method using only Term Frequency-Inverse Document Frequency (TF-IDF) features. Then, a data-stream clustering algorithm is applied for updating the system's knowledge from the incoming video feeds. Finally, all the actions are classified into different sets. Experiments and comparisons are conducted on the well known Weizmann and KTH datasets to show the efficacy of the proposed approach.

Multi-action recognition via stochastic modelling of optical flow and gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.

Multidimensionality of Actors in Business Networks: The Influence of Social Action in Pharmacy Networks in Finland

In the markets-as-networks approach business networks are conceived as dynamic actor structures, giving focus to exchange relationships and actors’ capabilities to control and co-ordinate activities and resources. Researchers have shared an understanding that actors’ actions are crucial for the development of business networks and for network dynamics. However, researchers have mainly studied firms as business actors and excluded individuals, although both firms and individuals can be seen as business actors. This focus on firms as business actors has resulted in a paucity of research on human action and the exchange of intangible resources in business networks, e.g. social exchange between individuals in social networks. Consequently, the current conception of business networks fails to appreciate the richness of business actors, the human character of business action and the import of social action in business networks. The central assumption in this study is that business actors are multidimensional and that their specific constitution in any given situation is determined by human interaction in social networks. Multidimensionality is presented as a concept for exploring how business actors act in different situations and how actors simultaneously manage multiple identities: individual, organisational, professional, business and network identities. The study presents a model that describes the multidimensionality of actors in business networks and conceptualises the connection between social exchange and human action in business networks. Empirically the study explores the change that has taken place in pharmaceutical retailing in Finland during recent years. The phenomenon of emerging pharmacy networks is highly contemporary in the Nordic countries, where the traditional license-based pharmacy business is changing. The study analyses the development of two Finnish pharmacy chains, one integrated and one voluntary chain, and the network structures and dynamics in them. Social Network Analysis is applied to explore the social structures within the pharmacy networks. The study shows that emerging pharmacy networks are multifaceted phenomena where political, economic, social, cultural, and historical elements together contribute to the observed changes. Individuals have always been strongly present in the pharmacy business and the development of pharmacy networks provides an interesting example of human actors’ influence in the development of business networks. The dynamics or forces driving the network development can be linked to actors’ own economic and social motives for developing the business. The study highlights the central role of individuals and social networks in the development of the two studied pharmacy networks. The relation between individuals and social networks is reciprocal. The social context of every individual enables multidimensional business actors. The mix of various identities, both individual and collective identities, is an important part of network dynamics. Social networks in pharmacy networks create a platform for exchange and social action, and social networks enable and support business network development.

Action Recognition from Motion Capture Data using Meta-cognitive RBF Network Classifier

Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).

Low-rank representation based action recognition

Human action recognition is an important problem in computer vision, which has been applied to many applications. However, how to learn an accurate and discriminative representation of videos based on the features extracted from videos still remains to be a challenging problem. In this paper, we propose a novel method named low-rank representation based action recognition to recognize human actions. Given a dictionary, low-rank representation aims at finding the lowestrank representation of all data, which can capture the global data structures. According to its characteristics, low-rank representation is robust against noises. Experimental results demonstrate the effectiveness of the proposed approach on several publicly available datasets.

Mediated action, narratives of risk-taking, and identity formation in adolescents with a visual impairment

The purpose of this project was to discern the inherent tension present in narratives told by adolescents with a visual impairment as they attempted to make sense of their experiences, specifically those surrounding risk. Mediated action, based on the foundational work of Vygotsky and Bakhtin, was used as both a theoretical and methodological approach; it is the theory that there are two components that constitute any human action: the "agent," or the person who is doing the acting, and the "mediational means" that he or she is using to accomplish the action in question. Tension ensues as neither is able to fully explain human behaviour. Ten adolescents with a visual impairment participated in a narrative interview, revealing numerous counter-narratives surrounding risk-taking, including "experimentation undertaken using good judgment." Participants offered examples of how they engaged, appropriated, resisted and transformed the dominant narratives of disability and adolescence in their identity formation.

Sensorimotor experience enhances automatic imitation of robotic action

Recent research in cognitive neuroscience has found that observation of human actions activates the ‘mirror system’ and provokes automatic imitation to a greater extent than observation of non-biological movements. The present study investigated whether this human bias depends primarily on phylogenetic or ontogenetic factors by examining the effects of sensorimotor experience on automatic imitation of non-biological robotic, stimuli. Automatic imitation of human and robotic action stimuli was assessed before and after training. During these test sessions, participants were required to execute a pre-specified response (e.g. to open their hand) while observing a human or robotic hand making a compatible (opening) or incompatible (closing) movement. During training, participants executed opening and closing hand actions while observing compatible (group CT) or incompatible movements (group IT) of a robotic hand. Compatible, but not incompatible, training increased automatic imitation of robotic stimuli (speed of responding on compatible trials, compared with incompatible trials) and abolished the human bias observed at pre-test. These findings suggest that the development of the mirror system depends on sensorimotor experience, and that, in our species, it is biased in favour of human action stimuli because these are more abundant than non-biological action stimuli in typical developmental environments.

Akrasia and the Aesthetic: Human Agency and the Site of Literature, 1760-1820

This study focuses on a series of foundational stylistic and formal innovations in eighteenth-century and Romantic literature, and argues that they can be cumulatively attributed to the distinct challenges authors faced in representing human action and the will. The study focuses in particular on cases of “acting against better judgment” or “failing to do what one knows one ought to do” – concepts originally theorized as “akrasia” and “weakness of the will” in ancient Greek and Scholastic thought. During the Enlightenment, philosophy increasingly conceives of human minds and bodies like systems and machines, and consequently fails to address such cases except as intractable or incoherent. Yet eighteenth-century and Romantic narratives and poetry consistently engage the paradoxes and ambiguities of action and volition in representations of akrasia. As a result, literature develops representational strategies that distinguish the epistemic capacities of literature as privileged over those of philosophy.

The study begins by centering on narratives of distempered selves from the 1760s. Jean-Jacques Rousseau’s Confessions and Laurence Sterne’s A Sentimental Journey narrate cases of knowingly and weakly acting against better judgment, and in so doing, reveal the limitations of the “philosophy of the passions” that famously informed sentimental literature at the time. These texts find that the interpretive difficulties of action demand a non-systematic and hermeneutic approach to interpreting a self through the genre of narrative. Rousseau’s narrative in particular informs William Godwin’s realist novels of distempered subjects. Departing from his mechanistic philosophy of mind and action, Godwin develops the technique of free indirect discourse in his third novel Fleetwood (1805) as a means of evoking the ironies and self-deceptions in how we talk about willing.

Romantic poetry employs the literary trope of weakness of will primarily through the problem of regretted inaction – a problem which I argue motivates the major poetic innovations of William Wordsworth and John Keats. While Samuel Taylor Coleridge sought to characterize his weakness of will in philosophical writing, Wordsworth turns to poetry with The Prelude (1805), revealing poetry itself to be a self-deceiving and disappointing form of procrastination. More explicitly than Wordsworth, John Keats identifies indolence as the prime symbol and basis of what he calls “negative capability.” In his letters and poems such as “On Seeing the Elgin Marbles” (1817) and “Ode on Indolence” (1819), Keats reveals how the irreducibly contradictory qualities of human agency speak to the particular privilege of “disinterested aesthetics” – a genre fitted for the modern era for its ability to disclose contradictions without seeking to resolve or explain them in terms of component parts.

Understanding the very nature of Project Management : a praxiological approach

Foreword: In this paper I call upon a praxiological approach. Praxeology (early alteration of praxiology) is the study of human action and conduct. The name praxeology/praxiologyakes is root in praxis, Medieval Latin, from Greek, doing, action, from prassein to do, practice (Merriam-Webster Dictionary). Having been involved in project management education, research and practice for the last twenty years, I have constantly tried to improve and to provide a better understanding/knowledge of the field and related practice, and as a consequence widen and deepen the competencies of the people I was working with (and my own competencies as well!), assuming that better project management lead to more efficient and effective use of resources, development of people and at the end to a better world. For some time I have perceived a need to clarify the foundations of the discipline of project management, or at least elucidate what these foundations could be. An immodest task, one might say! But not a neutral one! I am constantly surprised by the way the world (i.e., organizations, universities, students and professional bodies) sees project management: as a set of methods, techniques, tools, interacting with others fields – general management, engineering, construction, information systems, etc. – bringing some effective ways of dealing with various sets of problems – from launching a new satellite to product development through to organizational change.