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Viewpoint variation is a major challenge in video- based human action recognition. We exploit the simultaneous RGB and Depth sensing of RGB-D cameras to address this problem. Our technique capitalizes on the complementary spatio-temporal information in RGB and Depth frames of the RGB-D videos to achieve viewpoint invariant action recognition. We extract view invariant features from the dense trajectories...
The multilayer perceptrons (MLPs) have been widely used in many communication applications, however, the learning process of the multilayer perceptrons often becomes very slow, which is due to the existence of the singularities in the parameter space. As the singularities significantly affect the learning dynamics of MLPs, the standard gradient descent method is not Fisher efficient. In order to overcome...
An approach of a new methodology for skills transfer from machine to human is proposed in this research. This methodology transmits a haptic feed-back using vibrotactile perception to transfer motor skills using a parallel planar robot and virtual reality environments. During the experimentation, the participants tried to learn a specific motion trajectory given by the system. During the process,...
In dynamic manipulation, robots can manipulate objects without grasping by utilizing inertia effect. However, the trajectory planning for dynamic manipulation is a difficult issue due to dynamic constraint. Trajectory deformation considering dynamic constraint after original trajectories are generated is necessary for the issue. To realize such deformation methods, we introduce on sequence-to-sequence...
Accurate prediction of the future locations of the host vehicle as well as that of the surrounding objects is one of the key challenges in improving road traffic safety. The traditional approach for this task has been using physics-based motion models such as kinematic and dynamic models, the result of which is not reliable for long-term prediction. In this paper, we present simulation results demonstrating...
Currently deployed wireless and cellular positioning techniques are optimized for outdoor operation and cannot provide highly accurate location information in indoor environments. Meanwhile, new applications and services for mobile devices, including the recent Enhanced 911 (E911), require accurate indoor location information up to the room/suite level. In this work, a new system for improving indoor...
In this paper, we propose a CNN-based framework for online MOT. This framework utilizes the merits of single object trackers in adapting appearance models and searching for target in the next frame. Simply applying single object tracker for MOT will encounter the problem in computational efficiency and drifted results caused by occlusion. Our framework achieves computational efficiency by sharing...
We aim for zero-shot localization and classification of human actions in video. Where traditional approaches rely on global attribute or object classification scores for their zero-shot knowledge transfer, our main contribution is a spatial-aware object embedding. To arrive at spatial awareness, we build our embedding on top of freely available actor and object detectors. Relevance of objects is determined...
Manual annotations of temporal bounds for object interactions (i.e. start and end times) are typical training input to recognition, localization and detection algorithms. For three publicly available egocentric datasets, we uncover inconsistencies in ground truth temporal bounds within and across annotators and datasets. We systematically assess the robustness of state-of-the-art approaches to changes...
Advances in modeling and knowledge representation, data mining, semantic Internet, analytical methods and open data are the basis for new models of knowledge analysis. The growth of information and data exceeds the ability of organizations to analyze them. This problem is particularly expressed in terms of knowledge and learning processes. Analytical methods can be successfully applied in studying...
Fingerprint-based indoor localization has been intensively researched in the last decade, yet the labor-intensive and time-consuming site survey for radio map construction has impeded its practical implementations. Recently, crowdsourcing has been promoted as a promising approach to exploit casually collected samples for radio map construction, however, such samples may be labelled with erroneous...
Robots are widely used to help post-stoke patients conduct rehabilitation training for the motor function recovery. Because of the existence of repetitiveness in the rehabilitation training, a high-order iterative learning controller (ILC) is proposed for one hand rehabilitation robot in this paper. A series of tracking experiments are conducted to verify the effectiveness and superiority of the proposed...
Gait training is one of main means of rehabilitation of lower limb disfunction. Nevertheless, the promotion of clinical gait training is inhibited by the professional skill and labor consumption of physiatrist. It is with great practical value to design an automatic rehabilitation equipment which could increase the effectiveness and quality of training progress, meanwhile reduce labor costs. In this...
Progress in Multiple Object Tracking (MOT) has been historically limited by the size of the available datasets. We present an efficient framework to annotate trajectories and use it to produce a MOT dataset of unprecedented size. In our novel path supervision the annotator loosely follows the object with the cursor while watching the video, providing a path annotation for each object in the sequence...
Traffic behavioral monitoring within urban intersections is an essential issue in the Intelligent Transportation Systems (ITS) for a smart city. This paper investigates on gathering traffic information within an urban intersection where accidents frequently occur. In this paper, traffic pattern modeling, trajectory classification and a real-time vehicle tracker within the urban intersection are proposed...
Possible approaches to building the information and mathematical models to evaluate of the effectiveness and quality of the University are discussed in this paper. We characterize cycle of university management, determine the factors affecting the performance activity of universities, identify indicators of assessment of effectiveness and quality, formulate the problem of university management through...
In this paper, we consider parameter estimation in latent, spatiotemporal Gaussian processes using particle Markov chain Monte Carlo methods. In particular, we use spectral decomposition of the covariance function to obtain a high-dimensional state-space representation of the Gaussian processes, which is assumed to be observed through a nonlinear non-Gaussian likelihood. We develop a Rao-Blackwellized...
This paper presents a trajectory generation mechanism based on machine learning for a network of unmanned aerial vehicles (UAVs). For delay compensation, we apply an online regression technique to learn a pattern of network-induced effects on UAV maneuvers. Due to online learning, the control system not only adapts to changes to the environment, but also maintains a fixed amount of training data....
We propose a new framework for human action localization in video sequences. The option to not only detect but also localize actions in surveillance video is crucial to improving system's ability to manage high volumes of CCTV. In the approach, the action localization task is formulated the maximum-path finding problem in the directed spatio-temporal video-graph. The graph is constructed on the top...
The aim of this study is to develop a new method for hand gesture recognition using Leap Motion via deterministic learning. Efficient and accurate extraction and representation of gesture features are achieved. The recognition approach consists of two stages: a training stage and a recognition stage. In the training stage, hand gesture features representing hand motion dynamics, including spatial...
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