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We address the problem of full body human pose estimation in video. Most previous work consider body part, pose or trajectory of body part as basic unit to compose the pose sequence. In contrast, we consider tracklet of body part as the basic unit. Based on this medium granularity representation we develop a spatio-temporal graphical model to select an optimal tracklet for each part in each video...
We present a novel video representation for human action recognition by considering temporal sequences of visual words. Based on state-of-the-art dense trajectories, we introduce temporal bundles of dominant, that is most frequent, visual words. These are employed to construct a complementary action representation of ordered dominant visual word sequences, that additionally incorporates fine grained...
Sign Language Recognition (SLR) aims at translating the Sign Language (SL) into speech or text, so as to facilitate the communication between hearing-impaired people and the normal people. This problem has broad social impact, however it is challenging due to the variation for different people and the complexity in sign words. Traditional methods for SLR generally use handcrafted feature and Hidden...
Affect bursts are short, isolated and non-verbal expressions of affect expressed vocally or facially. In this paper we present an attempt at synthesizing audio affect bursts on several levels of arousal. This work concerns 3 different types of affect bursts: disgust, startle and surprised expressions. Data are first gathered for each of these affect bursts at two different levels of arousal each....
In this work we present a data-driven method for the reconstruction of dynamical systems from noisy and incomplete observation sequences. The key idea is to benefit from the availability of representative datasets of trajectories of the system of interest. These datasets provide an implicit representation of the dynamics of this system, in contrast to the explicit knowledge of the dynamical model...
We present a new approach to extracting low-dimensional neural trajectories that summarize the electrocorticographic (ECoG) signals recorded with high-channel-count electrode arrays implanted subdurally. In our approach, Hidden-Markov Factor Analysis (HMFA), a finite set of factor analyzers are used to model the relationship between the high-dimensional ECoG neural space and a low-dimensional latent...
Chronic pain is a disease that the patients suffers a lot in their daily life and it is difficult to be released completely. It is difficult to manage because pain can come anytime and it is unpredictable. However, the pain can be represented by the pain related behaviors such as guiding and abrupt actions. In this paper, we will develop a machine learning based system that can detect the pain related...
We propose a novel sequence score to determine to what extent neural activity is consistent with trajectories through latent ensemble states — virtual place fields — in an associated environment. In particular, we show how hidden Markov models (HMMs) can be used to model and analyze sequences of neural activity, and how the resulting joint probability of an observation sequence and an underlying sequence...
Pelvic organ prolapse is a major health problem in women where pelvic floor organs (bladder, uterus, small bowel, and rectum) fall from their normal position and bulge into the vagina. Dynamic Magnetic Resonance Imaging (DMRI) is presently used to analyze the organs' movements from rest to maximum strain providing complementary support for diagnosis. However, there is currently no automated or quantitative...
An approach to automatically detect bacteria division with temporal models is presented. To understand how bacteria migrate and proliferate to form complex multicellular behaviours such as biofilms, it is desirable to track individual bacteria and detect cell division events. Unlike eukaryotic cells, prokaryotic cells such as bacteria lack distinctive features, causing bacteria division difficult...
With the rapid development of mobile Internet, the way users access the network becomes diverse, which provide much convenience for us to collect huge amount of users' information. In this paper, we present a model of measuring relationship between two users in campus and build a wireless data analysis system called WiCloud to verify our model. This work has several potential applications such as...
One of the most challenging aspects of cooperative manipulation is coordination process between haptically-coupled subjects. The interaction force is believed to play a significant role in this process. In this paper, we propose a model for the interaction force and validate our model through a human study. The human study includes both bimanual and dyadic modes in three different scenarios, specifically...
This paper presents a novel method for classifying regions from human movements in service robots' working environments. The entire space is segmented subject to the class type according to the functionality or affordance of each place which accommodates a typical human behavior. This is achieved based on a grid map in two steps. First a probabilistic model is developed to capture human movements...
This paper focuses on identifying the operations of industrial manipulators that are often realised as robots with six degrees of freedom. The identification is based on the measurement of the power consumption of the whole robot without separating it to the individual axes. Such a case corresponds to industrial use cases. The robot is taken as an unobservable system with respect to its internal states...
This paper discusses the problem of one shot gesture recognition. This is relevant to the field of human-robot interaction, where the user's intentions are indicated through spontaneous gesturing (one shot) to the robot. The novelty of this work consists of learning the process that leads to the creation of a gesture, rather on the gesture itself. In our case, the context involves the way in which...
Visual Multiple Object Tracking (VMOT) is an important computer vision task which has gained increasing attention due to its academic and commercial potential. There are many different approaches have been proposed to solve the problem. Compared with single object tracking which focuses on appearance model, motion model and other factors, multiple object tracking shares these common challenges, and...
Recently, convolutional neural networks (CNNs) have been applied successfully to acoustic modelling in speech recognition. As the bottleneck features from CNNs contain inherently discriminative and rich context information, the standard approach is to augment the conventional acoustic features with the CNN bottleneck features in a tandem framework. To better capture the highly complex relationship...
We address the problem of feature space dimensionality reduction for the recognition of whole-body human action based on Hidden Markov Models. First, we describe how different features are derived from marker-based human motion capture and define a total number of 29 features with a total of 702 dimensions to describe human motion. We then propose a strategy for a systematic exploration of the space...
The problem of learning from demonstration in the case of partially observable external task parameters is addressed in this paper. Such a situation could be present in the daily life scenarios, where information regarding some task parameters are missing or partially available. In the first step, one dynamic movement primitives (DMP) model is learned for each demonstration trajectory. The parameters...
Non-intrusive load monitoring (NILM) aims to estimate the power or energy consumption for a collection of different appliances connected to a single power inlet, with only aggregated power profile being known. Such estimation is highly valuable for many potential applications in future smart grid. This paper proposes a bootstrap filtering based solver to work with smart meter data to solve the NILM...
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