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Learning from Demonstration (LfD) is addressed in this work in order to establish a novel framework for Human-Robot Collaborative (HRC) task execution. In this context, a robotic system is trained to perform various actions by observing a human demonstrator. We formulate a latent representation of observed behaviors and associate this representation with the corresponding one for target robotic behaviors...
Predicting the trajectory of a wide receiver in the game of American football requires prior knowledge about the game (e.g., route trees, defensive formations) and an accurate model of how the environment will change over time (e.g., opponent reaction strategies, motion attributes of players). Our aim is to build a computational model of the wide receiver, which takes into account prior knowledge...
Human activity recognition from full video sequence has been extensively studied. Recently, there has been increasing interest in early recognition or recognition from partial observation. However, from a small fraction of the video, it might be demanding if not even impossible to make a fine grained prediction of the activity that is taking place. Therefore, we propose the first method to predict...
In this preliminary study we aim to extract muscle synergies in spiral tracking task when performed with the non-dominant hand, and investigate the effect of training on muscle synergies. Three young subjects who were university students (all Females, 21±1 years old, right-handed) volunteered to participate in this study. The subjects were healthy with no history of neuromuscular disease. Each subject...
Fast growth of mobile sensing technologies, like GPS in smart phones, made capturing position data in the form of trajectories easy. But detecting anomalous trajectories, which are grossly different from remaining trajectories, is a major challenge in the surveillance domain and a big data problem. In the present work, a novel density-based method has been proposed and implemented for detection of...
The difficulties of data streams, i.e. Infinite length, the occurrence of concept-drift and the possible emergence of novel classes, are topics of high relevance in the field of recognition systems. To overcome all of these problems, the system should be updated continuously with new data while the amount of processing time should be kept small. We propose an incremental Parzen window kernel density...
In the automatic analysis of a tennis game, it is important to detect some anomalous match events, such as "fault serve" and "ball out", as these events are crucial in understanding the progress of a game. Audio information can be used to detect these events, but it is unreliable, because of the acoustic mismatch between the training and the test data and interfering noise caused...
In this paper, a new kind of Fisher Vector (FV) model, named Scale FV (ScaleFV), is proposed to ameliorate visual feature encoding for human action recognition. Although several researches have been proposed for feature encoding, the temporal scale information is almost ignored. Similar to the spatial scale information which has shown to be important in extracting and encoding visual features, the...
The use of autonomous navigation in Unmanned Aerial Vehicles is growing every day, in many areas, due to the low cost of its deployment and also does not require a pilot in a ground station. The most applied technique for autonomous navigation of Unmanned Aerial Vehicles is the joint use of Global Navigation Satellite System with Inertial Navigation System, an alternative for this technique is the...
Soccer is a very popular sport but also has a high rate of injuries. In this paper, player falling events in soccer videos are classified into five major categories. These categories have been identified by soccer coaches as the major mechanisms behind player injuries. Automatic detection of these events will be useful to coaches to plan specific training modules and to impart individual training...
This paper introduces an approach to generate ground-collision-free gait motion by learning a statistical model of walking motion and applies assist-as-needed (AAN) training scheme in learned statistical model which is efficient for robotic gait rehabilitation. The method utilizes a nonlinear dimensionality reduction technique, which is based on Gaussian process, to construct the model using gait...
Predictive modeling of human or humanoid movement becomes increasingly complex as the dimensionality of those movements grows. Dynamic Movement Primitives (DMP) have been shown to be a powerful method of representing such movements, but do not generalize well when used in configuration or task space. To solve this problem we propose a model called autoencoded dynamic movement primitive (AE-DMP) which...
At present, some training tools for endoscopic surgery have been commercialized such as box simulator and virtual training simulator. These training tools enable a trainee to practice various operation without any risks of a patient and have been utilized by unskilled surgeons in medical institutions. However, more effective training environments have been required by the doctors who engage in medical...
Dynamic Movement Primitives (DMPs)-originally a method for movement trajectory generation [1] has been also used for recognition tasks [2, 3]. However there has not been a systematic comparison between other recognition methods and DMPs using human movement data. This paper presents a comparison of commonly used Hidden Markov Model (HMM) based recognition with DMP based recognition using human generated...
This paper proposes a fast approach for traffic sign detection from video. First, we modify the image-based detector HHVCas to improve its accuracy and speed, then apply it to video-based detection with further acceleration by tracking. For the image-based detector, by optimizing the parameters in the cascade using an unsupervised approach, we achieve performance comparable to the state-of-the-art...
This paper presents an unsupervised approach for learning long-term human activities without requiring any user interaction (e.g., clipping long-term videos into short-term actions, labeling huge amount of short-term actions as in supervised approaches). First, important regions in the scene are learned via clustering trajectory points and the global movement of people is presented as a sequence of...
In this paper, we present a performance analysis of various descriptors suited to human gait analysis in Rotating Multi-Beam (RMB) Lidar measurement sequences. The gait descriptors for training and recognition are observed and extracted in realistic outdoor surveillance scenarios, where multiple pedestrians walk concurrently in the field of interest, their trajectories often intersect, while occlusions...
In order to meet the special control requests of an intercept missile, a new boost-phase guidance law is proposed based on the theory of pesudospcetral method and artificial neural network. A group of optimal trajectories with multiple constraints, obtained by hp-adaptive pesudospcetral method, is used as samples to train the neural network. To show the effect of training patterns on the guidance...
Indoor localization is a key topic for mobile computing. However, it is still very difficult for the mobile sensing community to compare state-of-art Indoor Positioning Systems due to the scarcity of publicly available databases. Magnetic field-based methods are becoming an important trend in this research field. Here, we present UJIIndoorLoc-Mag database, which can be used to compare magnetic field-based...
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...
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