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The recognition of human gestures in image sequences is an important and challenging problem that enables a host of human-computer interaction applications. This paper describes a gesture recognition algorithm based on the particle filters, namely CONDENSATION. The particle filter is more efficient than any other tracking algorithm because the tracking mechanism follows Bayesian estimation rule of conditional probability propagation. We used two models for the evaluation of particle filter and apply the MATLAB for the preprocessing of the image sequence. But we implement the particle filter using the C++ to get the high speed processing. In the experimental results, it is demonstrated that the proposed algorithm prove to be robust in the cluttered environment. [ABSTRACT FROM AUTHOR]

In this paper, we propose a state transition model using context based approach for gesture analysis. This method defines the analysis situation as five different states; NULL, OBJECT, POSTURE, LOCAL, and GLOBAL. We first infer the situation of the system by estimating the transition of the state model, and then apply different analysis algorithms according to the system state. Gestures are analyzed with the queue-based matching method which is newly proposed in the paper instead of general gesture spotting algorithms. In the algorithm, movement of feature points; face and both hands, is compared directly, so gesture can be recognized quickly without applying any constraints for real world application. The transition of the states can be interpreted as the context of motion and that is estimated with conditional probability between the states in the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we have developed the MHDA scheme for data association. This scheme is important in providing a computationally feasible alternative to complete enumeration of JPDA which is intractable. We have proved that given an artificial measurement and track's configuration, MHDA scheme converges to a proper plot in a finite number of iterations. Also, a proper plot which is not the global solution can be corrected by re-initializing one or more times. In this light, even if the performance is enhanced by using the MHDA, we also note that the difficulty in tuning the parameters of the MHDA is critical aspect of this scheme. The difficulty can, however, be overcome by developing suitable automatic instruments that will iteratively verify convergence as the network parameters vary. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm using Gaussian Mixture Model (GMM) for foreground segmentation which can differentiate shadow region from objects with simple criteria. In the algorithm, we have utilized the Improved HLS (IHLS) color space model as the fundamental description for image, instead of using raw RGB data. IHLS color space has an advantage over the standard RGB space to recognize shadow region from object by utilizing luminance and saturation-weighted hue information directly, without any calculation of chrominance and luminance. By exploiting this feature in GMM, we obtain adaptive background model with good sensitivity to color changes and shadow. [ABSTRACT FROM AUTHOR]

In this paper, we describe an algorithm which can automatically recognize human gesture for Human-Robot interaction by utilizing attention control method. In early works, many systems for recognizing human gestures work under many restricted conditions. To solve the problem, we propose a novel model called APM(Active Plane Model),which can represent 3D and 2D gesture information simultaneously. Also we present the state transition algorithm for selection of attention. In the algorithm, first we obtain the information about 2D and 3D shape by deforming the APM, and then the feature vectors are extracted from the deformed APM. The next step is constructing a gesture space by analyzing the statistical information of training images with PCA. And then, input images are compared to the model and individually symbolized to one of the pose model in the space. In the last step, the symbolized poses are recognized with HMM as one of model gestures. The experimental results show that the proposed algorithm is very efficient to construct intelligent interface system. [ABSTRACT FROM AUTHOR]