The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Most of the available robot programming by demonstration (PbD) approaches focus on learning a single task, in a given environmental situation. In this paper, we propose to learn multiple tasks together, within a common environment, using one of the available PbD approaches. Task-parameterized Gaussian mixture model (TP-GMM) is used at the core of the proposed approach. A database of TP-GMMs will be...
In this paper, we compare different deep neural network approaches for motion prediction within a highway entrance scenario. The focus of our work lies on models that operate on limited history of data in order to fulfill the Markov property1 and be usable within an integrated prediction and motion planning framework for automated vehicles. We examine different model structures and feature combinations...
Traditional location-based service profiles user's traits by looking for patterns in historical mobility behaviors. Yet, from time to time, people are adventurous and would often like to go to unvisited places, or follow new transition paths. At that time, their next movements will be inconsistent with any previous patterns, making location-based recommendations inaccurate and irrelevant to user's...
Automatic identification of the relevant frames of references (or external task parameters) in programming by demonstration using the task-parameterized Gaussian mixture regression (TP-GMM) is addressed in this paper. While performing a given task, there may be several external task parameters, some of which are relevant to the specific task, while some others are not relevant. Identifying the irrelevant...
This paper presents an efficient model for combining automotive trajectory planning with predicted environment interactions, named progressively interacting trajectories (PITRA). It allows to plan trajectories for fully-automated vehicles by actively considering how other traffic participants will react to the trajectory, while retaining many of the advantages of variational trajectory optimization...
Human motion is fast and hard to predict. To implement a provably safe collision-avoidance strategy for robots in collaborative spaces with humans, an overapproximative prediction of the occupancy of the human is required, which needs to be calculated faster than real time. We present a method for computing volumes containing the entire possible future occupancy of the human, given its state, faster...
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...
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...
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...
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...
In this work, we study the problem of anomaly detection of the trajectories of objects in a visual scene. For this purpose, we propose a novel representation for trajectories utilizing covariance features. Representing trajectories via co-variance features enables us to calculate the distance between the trajectories of different lengths. After setting this proposed representation and calculation...
We present a first implementation of a framework for the exploration of stylistic variations in intangible heritage, recorded through motion capture techniques. Our approach is based on a statistical modelling of the phenomenon, which is then presented to the user through a reactive stylistic synthesis, visualised in real-time on a virtual character. This approach enables an interactive exploration...
Robust dynamic gesture recognition algorithm is of great value for kinds of intelligent interactive systems. Most current researches on this field are based on trajectory time-series, which is unstable and limited. In this paper, we proposed a novel method to realize dynamic gesture recognition by analyzing the static trajectory images with Convolutional Neural Networks (CNN). First of all, a new...
This paper presents a novel manipulation trajectory generating algorithm that constructs trajectories from learned motion harmonics and user defined constraints. The algorithm uses functional eigenanalysis to learn motion harmonics from demonstrated motions and then use the motion harmonics to compute the optimal trajectory that resembles the demonstrated motions and also satisfies the constraints...
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...
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...
Recent efforts in the field of intervention-autonomous underwater vehicles (I-AUVs) have started to show promising results in simple manipulation tasks. However, there is still a long way to go to reach the complexity of the tasks carried out by ROV pilots. This paper proposes an intervention framework based on parametric Learning by Demonstration (p-LbD) techniques in order to acquire multiple strategies...
Monitoring the dynamical behavior of receptors and lig-ands via single-molecule fluorescence microscopy allows quantifying the interactions between these two subcellular structures at a very high spatial and temporal resolution. We have developed a probabilistic approach to determine the positions of receptors and ligands over time in two-channel image sequences of small protein complexes and single...
Human action recognition from video input has seen much interest over the last decade. In recent years, the trend is clearly towards action recognition in real-world, unconstrained conditions (i.e. not acted) with an ever growing number of action classes. Much of the work so far has used single frames or sequences of frames where each frame was treated individually. This paper investigates the contribution...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.