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Adaptation is an essential capability for intelligent robots to work in new environments. In the learning framework of Programming by Demonstration (PbD) and Reinforcement Learning (RL), a robot usually learns skills from a latent feature space obtained by dimension reduction techniques. Because the latent space is optimized for a specific environment during the training phase, it typically contains...
This paper focuses on recognition and prediction of human reaching motion in industrial manipulation tasks. Several supervised learning methods have been proposed for this purpose, but we seek a method that can build models on-the-fly and adapt to new people and new motion styles as they emerge. Thus, unlike previous work, we propose an unsupervised online learning approach to the problem, which requires...
This paper proposes a probabilistic generative model of a sequence of vectors called the latent trajectory hidden Markov model (HMM). While a conventional HMM isonly capable of describing piecewise stationary sequences of data vectors, the proposed model is capable of describing continuously time-varying sequences of data vectors, governed by discrete hidden states. This feature is noteworthy in that...
Robots navigating in a social way should use some knowledge about common motion patterns of people in the environment. Moreover, it is known that people move intending to reach certain points of interest, and machine learning techniques have been widely used for acquiring this knowledge by observation. Learning algorithms such as Growing Hidden Markov Models (GHMMs) usually assume that points of interest...
This paper presents a novel approach to modeling the dynamics of human movements with a grid-based representation. The model we propose, termed as Multi-scale Conditional Transition Map (MCTMap), is an inhomogeneous HMM process that describes transitions of human location state in spatial and temporal space. Unlike existing work, our method is able to capture both local correlations and long-term...
This paper proposes the use of a kernel density estimation to measure similarities between trajectories. The similarities are then used to predict the future locations of a target. For a given environment with a history of previous target trajectories, the goal is to establish a probabilistic framework to predict the future trajectory of currently observed targets based on their recent moves. Instead...
We present a method for segmenting a set of unstructured demonstration trajectories to discover reusable skills using inverse reinforcement learning (IRL). Each skill is characterised by a latent reward function which the demonstrator is assumed to be optimizing. The skill boundaries and the number of skills making up each demonstration are unknown. We use a Bayesian nonparametric approach to propose...
Learning motor skills from multiple demonstrations presents a number of challenges. One of those challenges is the occurrence of occlusions and lack of sensor coverage, which may corrupt part of the recorded data. Another issue is the variability in speed of execution of the demonstrations, which may require a way of finding the correspondence between the time steps of the different demonstrations...
Web service application is becoming a popular and important software application on the web. With the advent of more and more service application using on the web, it is a crucial challenge to use an effective diagnostic technology to localize the service faults. To improve the diagnosis capability for service application software, we build a second-order hidden Markov diagnosis model by considering...
Existing methods have addressed the issue of detecting abnormal events at a smart home for medical care or security monitoring services extensively in the past decades. However, most of approaches use wearable sensors that require users to be equipped with the sensor devices at every moment. If the monitored users stop or pause the sensors, any abnormal events are not able to be detected. The use...
In this paper we present a novel approach to detect people meeting. The proposed approach works by translating people behaviour from trajectory information into semantic terms. Having available a semantic model of the meeting behaviour, the event detection is performed in the semantic domain. The model is learnt employing a soft-computing clustering algorithm that combines trajectory information and...
A method for online identification of group of moving objects in the video is proposed in this paper. This method at each frame identifies group of tracked objects with similar local instantaneous motion pattern using spectral clustering on motion similarity graph. Then, the output of the algorithm is used to detect the event of more than two object moving together as required by PETS2015 challenge...
In this paper we perform an analysis of human behaviour for people standing in a queue with the aim to discover, in an unsupervised way, ongoing unusual or suspicious activities. The main activity types we focus on are detecting people loitering around the queue and people going against the flow of the queue or undertaking a suspicious path. The proposed approach works by first detecting and tracking...
There are many shape-similar gestures which cause errors in the process of hand gesture recognition. In this paper, a new method which can distinguish the similar gestures was proposed. The information of motion trajectory is captured by a leap motion in three-dimension space, and the orientation characteristics are quantified and coded as the feature. Then the Hidden Markov Model (HMM) algorithm...
In recent years, semi-autonomous vehicle control solutions have been aggressively developed in the form of various advanced driver assistance systems. It is expected that these developments will facilitate the eventual customer acceptance of fully autonomous vehicles. In this paper, we present a configuration of a nonlinear model predictive control scheme for predictive driver assistance. The human...
In this paper, we propose a new system for isolated sign language recognition (SLR) and continuous SLR. In isolated SLR, Histogram of Oriented Displacement is used to describe the trajectories, and multi-SVM is adopted for classification. In continuous SLR, we propose a Dynamic Programming method with warping templates obtained by Dynamic Time Warping (DTW) algorithm. We evaluate our approach with...
Deep neural network(DNN) has achieved a great success in automatic speech recognition(ASR), and it can be regarded as a joint model combining the nonlinear feature transformation and the log-linear classifier. Recently DNN is adopted as a regression model to enhance the distorted feature in noisy condition and the enhanced feature is utilized to improve the performance of DNN based ASR. Previous work...
Human computer interaction (HCI) and sign language recognition (SLR), aimed at creating a virtual reality, 3D gaming environment, helping the deaf-and-mute people etc., extensively exploit the use of hand gestures. Segmentation of the hand part from the other body parts and background is the primary need of any hand gesture based application system; but gesture recognition systems are often plagued...
In this paper, we present a novel framework to detect abnormal behaviors in surveillance videos by using fuzzy clustering and multiple Auto-Encoders (FMAE). As detecting abnormal behaviors is often treated as an unsupervised task, how to describe normal patterns becomes the key point. Considering there are many types of normal behaviors in the daily life, we use the fuzzy clustering technique to roughly...
Sign Language Recognition (SLR) targets on interpreting the sign language into text or speech, so as to facilitate the communication between deaf-mute people and ordinary people. This task has broad social impact, but is still very challenging due to the complexity and large variations in hand actions. Existing methods for SLR use hand-crafted features to describe sign language motion and build classification...
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