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As the demands for video surveillance cameras and systems have been greatly increased, more sophisticated techniques for handling video clips from static cameras are required. Trajectory retrieval is one of the relevant applications that exploit such video clips. In this paper, we introduce a system for retrieving object trajectories shown in video clips, captured by static cameras. The system shows...
Particle filtering - perhaps more properly named Sequential Monte Carlo - approaches have a strong potential for signal and image processing applications. A problem of great practical significance in this field, which remains largely unsolved as of today, is the estimation of fixed model parameters based on the output of sequential simulations. In this contribution, we investigate maximum likelihood...
In this paper we have briefly reviewed the Statistical Parametric Speech Synthesis (SPSS ), based on hidden Markov model. The non-mathematical introduction of SPSS have been introduced. Have emphasized the recent emerging techniques used in SPSS like Autoregressive HMM model, Gaussian Process Regression(GPR), Neural Autoregressive Distribution Estimators (NADE) overcoming Restricted Boltzmann Machines...
This paper proposes a modified post-filter to recover a Modulation Spectrum (MS) in HMM-based speech synthesis. To alleviate the over-smoothing effect which is one of the major problems in HMM-based speech synthesis, the MS-based post-filter has been proposed. It recovers the utterance-level MS of the generated speech trajectory, and we have reported its benefit to the quality improvement. However,...
A credibility evaluation of software behavior based on behavioral attribute distance is presented. Software behavior is described by defining software attribute, and attributes of node are made a quantization disposal. Through an evaluation process consists of fixed-point evaluation, vertical evaluation and horizontal evaluation based on behavioral attribute distance, we conducted a consistency evaluation...
In this paper, a novel 3-D motion trajectory signature is introduced to serve as an effective description to the raw trajectory. More importantly, based on the trajectory signature, a probabilistic model-based cluster signature is further developed for modeling a motion class. The cluster signature is a mixture model-based motion description that is useful for motion class perception, recognition...
A signal processing approach for modeling vehicle trajectory during lane changing driving is discussed. Because individual driving habits are not a deterministic process, we developed a stochastic method. The proposed model consists of two parts: a dynamic system represented by a hidden Markov model and a cognitive distance space derived from the range distance distribution. The first part models...
We propose the use of hidden Markov models (HMMs) to qualify arm gestures. A HMM is trained based on the reference or correct gesture. Then, samples of the gesture that we want to score are used to train a second HMM. Both HMMs are compared, and a measure of their similarity is used to qualify the gesture. We used 3 different metrics to compare HMMs: Levinson, Kullback-Leibler and Porikli. For this,...
This paper presents a method to recognize and generate sequential motions for object manipulation such as placing one object on another or rotating it. Motions are learned using reference-point-dependent probabilistic models, which are then transformed to the same coordinate system and combined for motion recognition/generation. We conducted physical experiments in which a user demonstrated the manipulation...
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