Support Vector Motion Clustering

This work was supported in part by the Erasmus Mundus Joint Doctorate in Interactive and Cognitive Environments (which is funded by the EACEA Agency of the European Commission under EMJD ICE FPA n 2010-0012) and by the Artemis JU and the UK Technology Strategy Board through COPCAMS Project under Grant 332913

Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine

Activity-Based Computing aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject’s body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence (AmI) in daily activity monitoring for elderly people. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone inertial sensors. As these mobile phones are limited in terms of energy and computing power, we propose a novel hardware-friendly approach for multiclass classification. This method adapts the standard Support Vector Machine (SVM) and exploits fixed-point arithmetic for computational cost reduction. A comparison with the traditional SVM shows a significant improvement in terms of computational costs while maintaining similar accuracy, which can contribute to develop more sustainable systems for AmI.Peer ReviewedPostprint (author's final draft

Left/Right Hand Segmentation in Egocentric Videos

Wearable cameras allow people to record their daily activities from a user-centered (First Person Vision) perspective. Due to their favorable location, wearable cameras frequently capture the hands of the user, and may thus represent a promising user-machine interaction tool for different applications. Existent First Person Vision methods handle hand segmentation as a background-foreground problem, ignoring two important facts: i) hands are not a single "skin-like" moving element, but a pair of interacting cooperative entities, ii) close hand interactions may lead to hand-to-hand occlusions and, as a consequence, create a single hand-like segment. These facts complicate a proper understanding of hand movements and interactions. Our approach extends traditional background-foreground strategies, by including a hand-identification step (left-right) based on a Maxwell distribution of angle and position. Hand-to-hand occlusions are addressed by exploiting temporal superpixels. The experimental results show that, in addition to a reliable left/right hand-segmentation, our approach considerably improves the traditional background-foreground hand-segmentation

Unsupervised Understanding of Location and Illumination Changes in Egocentric Videos

Wearable cameras stand out as one of the most promising devices for the upcoming years, and as a consequence, the demand of computer algorithms to automatically understand the videos recorded with them is increasing quickly. An automatic understanding of these videos is not an easy task, and its mobile nature implies important challenges to be faced, such as the changing light conditions and the unrestricted locations recorded. This paper proposes an unsupervised strategy based on global features and manifold learning to endow wearable cameras with contextual information regarding the light conditions and the location captured. Results show that non-linear manifold methods can capture contextual patterns from global features without compromising large computational resources. The proposed strategy is used, as an application case, as a switching mechanism to improve the hand-detection problem in egocentric videos.Comment: Submitted for publicatio

Who is a Better Tutor? Gaze Hints with a Human or Humanoid Tutor in Game Play

In this paper, we present a study that analyses the effects of robot or human gaze hints on people's choices in a card game. We asked human participants to play a matching card game in the presence of a human or a robotic tutor. Our aim was to find out if gaze hints provided by the tutor can direct the attention and influence the choices of the human participants. The results show that participants performed significantly better when they received gaze hints from a tutor than when they did not. Furthermore, we found that people identified the tutor hints more often in robot condition than in human condition and, as a result, performed significantly better.Postprint (published version

The Evolution of First Person Vision Methods: A Survey

The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio

A new representation of emotion in affective computing

In the recent years, increasing attention has been paid to the area of affective computing, which deals with the complex phenomenon of human emotion. Therefore, a model for describing, structuring, and categorizing emotional states of users is required. The dimensional emotion theory is one of widely used theoretical foundations for categorization of emotions. According to the dimensional theory, emotional states are projected to the affective space, which has two dimensions: valence and arousal. In order to navigate in the affective space, Cartesian coordinate system is used, where emotion quality is defined by combination of valence and arousal. In this paper, we propose another representation of the affective space with polar coordinate system. The key advantages of such a representation include (1) capability to account not only for emotion quality, but also for emotion intensity, (2) reasonable explanation of the location of neutral emotion in the affective space, and (3) straightforward interpretation of the meaning of an emotional state (quality defined by angle and intensity defined by distance from the origin). Although in our experiment most of the induced motions can be differentiated with polar coordinate system, further investigation is still needed to find out either Cartesian or polar coordinates system represents affective space better in practice

Average consensus-based asynchronous tracking

Target tracking in a network of wireless cameras may fail if data are captured or exchanged asynchronously. Unlike traditional sensor networks, video processing may generate significant delays that also vary from camera to camera. Moreover, the continuous and rapid change of the dynamics of the consensus variable (the target state) makes tracking even more challenging under these conditions. To address this problem, we propose a consensus approach that enables each camera to predict information of other cameras with respect to its own capturing time-stamp based on the received information. This prediction is key to compensate for asynchronous data exchanges. Simulations show the performance improvement with the proposed approach compared to the state of the art in the presence of asynchronous frame captures and random processing delays

Experiencing the world with archetypal symbols: A new form of aesthetics.

According to the theories of symbolic interactionism, phenomenology of perception and archetypes, we argue that symbols play the key role in translating the information from the physical world to the human experience, and archetypes are the universal knowledge of cognition that generates the background of human experience (the life-world). Therefore, we propose a conceptual framework that depicts how people experience the world with symbols, and how archetypes relate the deepest level of human experience. This framework indicates a new direction of research on memory and emotion, and also suggests that archetypal symbolism can be a new resource of aesthetic experience design.Postprint (published version