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The vehicle trajectory dataset is often contaminated by GPS errors and low sampling rate. Consequently, it is important to cleanse the trajectory dataset before it can be used for any research or application. In this paper, we propose a HMM based system to cleanse and rebuild the missing traveling routes of vehicles in a given trajectory dataset. Considering the candidates of each entry as variables,...
Modern city intelligent transportation system urgently demands high accuracy real-time map matching methods. Because of inaccurately measured locations, a GPS point can be assigned to many road segments depending on the topology of the road network and GPS measurement, especially in the complex urban traffic environment. Taking some object motion laws, such as speed limitations and acceleration constraints,...
Chronic pain is a disease that the patients suffers a lot in their daily life and it is difficult to be released completely. It is difficult to manage because pain can come anytime and it is unpredictable. However, the pain can be represented by the pain related behaviors such as guiding and abrupt actions. In this paper, we will develop a machine learning based system that can detect the pain related...
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...
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 this paper we propose synchronization rules between acoustic and visual laughter synthesis systems. This work follows up our previous studies on acoustics laughter synthesis and visual laughter synthesis. The need of synchronization rules comes from the constraint that in laughter, HMM-based synthesis of laughter cannot be performed using a unified system where common transcriptions may be used...
Classification of moving objects for video surveillance applications still remains a challenging problem due to the video inherently changing conditions such as lighting or resolution. This paper proposes a new approach for vehicle/pedestrian object classification based on the learning of a static kNN classifier, a dynamic Hidden Markov Model (HMM)-based classifier, and the definition of a fusion...
Nowadays gesture recognition is a hot topic in the field of human-computer interaction (HCI). HCI develop very fast, and also brings surprise to us constantly. In this paper, we propose a novel approach based on improved HMMs with entropy to recognize the 3D gesture. In our method, there are two steps to recognize a gesture: 1. detect the key nodes of body with extracting the skeleton point. A low-pass...
In traditional localization systems, it is required that moving object carries a device to transmit or receive signals, and then localization system is able to locate an object based on signal strength it received. In this paper, we propose a new passive localization and tracking approach based on RFID with sparse reference tags, which can estimate the location of moving objects by detecting and analyzing...
Sign languages (SL) are the most accomplished forms of gestural communication. Therefore, their automatic analysis is a real challenge which is interestingly implied to their lexical and syntactic organization levels. Statements dealing with sign language occupy a significant interest in the Automatic Natural Language Processing (ANLP) domain. In this work, we are dealing with sign language recognition,...
In this paper, we present a new method to automatically discover recurrent activities occurring in a video scene, and to identify the temporal relations between these activities, e.g. to discover the different flows of cars at a road intersection, and to identify the traffic light sequence that governs these flows. The proposed method is based on particle-based trajectories, analyzed through a cascade...
In this paper, we propose a nonparametric grammar based framework for analyzing trajectories, aiming to discover the motion pattern of objects and assist human understanding. The framework works in three steps. 1) Raw trajectories are smoothed to eliminate noise, and then, points and segments are sampled as primitive units. 2) The primitive units are clustered based on DPM and HDP-HMM, in order to...
Traffic management is a serious issue in the intelligent transportation systems (ITS). One of the most significant current discussions is traffic incident detection. We have developed an algorithm, referred to vehicle detection based on level set theory and background subtraction, accurate contour of moving object is obtained. The Kalman filtering is applied to predict the possible trajectories of...
This paper proposes motion primitives for designing a gesture set in a gesture recognition system as Human-Robot Interface (HRI). Based on statistical analyses of angular tendency of hand movements in sign languages and hand motions in practical gestures, we construct four motion primitives as building blocks for basic hand motions. By combining these motion primitives, we design a discernable ‘fundamental...
A key prerequisite of automatic video indexing and summarisation is the description of events and actions. In the context of many sports, the motion of the ball and agents plays an essential role in describing events. However, the only existing solution for the tennis event recognition problem in the literature is the work in which relies on a set of heuristic rules such as proximity between ball...
In this paper we propose a view invariant hand gesture recognition algorithm based on the assumption that the gesture trajectory is almost in a plane, which we call principal gesture plane. We use Least Squares Method to estimate the plane and project the 3D trajectory onto it. The viewpoint-dependent problem is solved by the projection. HMMs are chosen to model the gestures. We have evaluated the...
This paper provides a new method for modeling, clustering, and generalizing complex pseudo-periodic motions in a Robot Programming by Demonstration (PbD) framework. Relevant features of the trajectories are extracted by applying a linear mapping off the surface part using Moving Window Principal Component Analysis. A Hidden Markov Model is used for segmentation and temporal clustering of feature data...
Human robot interaction has attracted significant attention over the last couple of years. An important aspect of such robotic systems is to share the working space with humans and carry out the tasks in a socially acceptable way. In this paper, we address the problem of fusing socially acceptable behaviours into robot path planning. By observing an environment for a while, the robot learns human...
A real-time hand gesture recognition system is developed for human-robot interaction of service robot. The proposed system is mainly composed of two subsystems: one for gesture recognition, and the other for the classification of the gesture motion. The system first uses a cascade classifier to locate the potential hand region from video frame. Then, Gabor wavelets transformation is applied to extract...
MIMO radar is a new radar technique developed recently. It can achieve better detection performance than conventional phased radar. Based on the scattering statistic of clutter and man-made target, we propose that hidden Markov models (HMM) can be used to model the clutter and target signals, and that the HMMs can be used to detect target. Simulation results verify its efficiency.
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