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.
Lower-limb exoskeleton is widely used for assisting walk in rehabilitation field. One key problem for exoskeleton control is to model and predict the suitable gait trajectories of wearer. In this paper, we propose a Deep Rehabilitation Gait Learning (DRGL) for modeling the knee joints of lower-limb exoskeleton, which firstly leverage Long-Short Term Memory (LSTM) to learn the inherent spatial-temporal...
This paper proposes an end-to-end framework, namely fully convolutional recurrent network (FCRN) for handwritten Chinese text recognition (HCTR). Unlike traditional methods that rely heavily on segmentation, our FCRN is trained with online text data directly and learns to associate the pen-tip trajectory with a sequence of characters. FCRN consists of four parts: a path-signature layer to extract...
We model dyadic (two-person) interactions by discriminatively training a spatio-temporal deformable part model of fine-grained human interactions. All interactions involve at most two persons. Our models are capable of localizing human interactions in unsegmented videos, marking the interactions of interest in space and time. Our contributions are as follows: First, we create a model that localizes...
In this paper, we focus on the problem of group detection in crowd, which is a task of partitioning a set of pedestrians in a scene into small subsets called groups based on their trajectories. Most of previous methods use only a single model for representing a relationship between trajectories of pedestrians who belong to the same group. However, such relationship would vary depending on the activity...
Surveillance systems are widely spread in many public and private places of social life such as streets, offices, universities, hospitals and parking lots. The amount of data to be processed grows together with the need of skilled experts able to interpret it. For this reason, the scientific community put a great effort in conceiving approaches able to detect anomalies automatically. Whichever the...
Large vocabulary gesture recognition using a training set of limited size is a challenging problem in computer vision. With few examples per gesture class, researchers often employ exemplar-based methods such as Dynamic Time Warping (DTW). This paper makes two contributions in the area of exemplar-based gesture recognition: 1) it introduces Multiple-Pass DTW (MP-DTW), a method in which scores from...
Wearable devices such as smartwatches have become popular. The accelerometer embedded in smartwatch can record the movement of hand, so that it may lead to privacy compromise when performing sensitive inputting on keyboard with hand wearing smartwatch. The existing inference attack collects smartwatch's accelerometer readings which correspond to the movement of hand when inputting a numeric password,...
Inspired by Gustave Lebon's idea of crowds as single-minded entities, we present a novel approach to describe the behavior of a crowd as a single entity, based on the global movement of the entire aggregate of people conforming the crowd. The present work significantly differs from existing literature where the behavior of single individuals within the crowd are the building blocks to describe crowd...
There is a big challenge in online multi-object tracking-by-detection, which caused by frequent occlusions, false alarms or miss detections and other factors. In this paper, we proposed an improved fast online multi-object tracking method through taking into account the results of multiple single-object trackers and detections synthetically. To solve the fixed scale problem of conventional kernelized...
Goal-finding in an unknown maze is a challenging problem for a Reinforcement Learning agent, because the corresponding state space can be large if not intractable, and the agent does not usually have a model of the environment. Hierarchical Reinforcement Learning has been shown in the past to improve tractability and learning time of complex problems, as well as facilitate learning a coherent transition...
Scholarships and financial aids in modern universities are the basic administrative plans to ensure and promote the completion of academic training and studies for students. Traditional grants allocation procedures are based on manual determination, which costs lots of human resources. In this paper, we investigate an assistance model for helping improve the scheme of granting. We first collect students...
We investigate the use of deep artificial neural networks to approximate the optimal state-feedback control of continuous time, deterministic, non-linear systems. The networks are trained in a supervised manner using trajectories generated by solving the optimal control problem via the Hermite-Simpson transcription method. We find that deep networks are able to represent the optimal state-feedback...
In this paper, we propose a mutual framework that combines two state-of-the-art visual object tracking algorithms. Both trackers benefit from each other's advantage leading to an efficient visual tracking approach. Many state-of-the-art trackers have poor performance due to rain, fog or occlusion in real-world scenarios. Often, after several frames, objects are getting lost, only leading to a short-term...
Recent works of non-rigid registration have shown promising applications on tasks of deformable manipulation. Those approaches use thin plate spline-robust point matching (TPS-RPM) algorithm to regress a transformation function, which could generate a corresponding manipulation trajectory given a new pose/shape of the object. However, this method regards the object as a bunch of discrete and independent...
Human activity recognition plays an important role in personal assistive robot, being able to recognize human activity and perform corresponding assistive action is a great challenges for personal assistive robot. Human body is an articulated system of rigid segments that can be divided into five parts, but many existing methods always identify actions based on the motion trajectories of whole body...
In this work, we present an approach to learn cost maps for driving in complex urban environments from a large number of demonstrations of human driving behaviour. The learned cost maps are constructed directly from raw sensor measurements, bypassing the effort of manually designing cost maps as well as features. When deploying the cost maps, the trajectories generated not only replicate human-like...
This paper suggests a mapless indoor localization using wifi received signal strength (RSS) of a smartphone, collected by multiple people. A new trajectory learning algorithm by combining a dynamic time warping and a machine learning technique is proposed in order to generate an alternative map. Moreover, we combine particle filter and Gaussian process (GP) for the position estimation, because it...
In recent years, e-rehabilitation has become an emerging topic, firstly because of an increasing demand, secondly because of improved sensor systems and higher computational performance. Furthermore, due to the lack of therapists in Germany, an adequate supervision of the therapy is often impossible. A tracking of physiological parameters, such as the heart rate, can contribute to an improved evaluation...
The most important task of the modern education system is implementation of individual educational trajectory of each student. In this regard, adaptation of the educational process to personality development of each student becomes extremely necessary. One of the ways of tackling the ensuing problem is e-learning as a model of organization of interaction between the teacher, student and information...
The trajectory piecewise-linear (TPWL) method is a promising model order reduction method for nonlinear systems. Since the quality of linearization points has an important influence on the accuracy and complexity of the obtained reduced-order model, how to select them reasonably is crucially important in TPWL method. In our previous study, we have presented a linearization point selection method based...
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.