Search

A real-time vision system operating at an outdoor swimming pool is presented in this paper. The system is designed to automatically recognize different swimming activities and to detect occurrence of early drowning incidents. We have named this system the Drowning Early Warning System (DEWS). One key challenge we faced in the problem is the relatively high level of noise in the steps of foreground detection and behavior recognition. Therefore, a set of methods in the fields of background subtraction, denoising, data fusion and blob splitting are proposed, which have been motivated by characteristics of aquatic background and crowded scenario at the pool. In the step to detect an early drowning incident, visual indicators of distress and drowning are incorporated through a set of foreground descriptors. A module comprising data fusion and hidden Markov modeling is developed to learn unique traits of different swimming behaviors, in particular, those early drowning events. The experiment of this work reports realistic on-site evaluations performed. Examples of interesting behaviors, i.e., distress, drowning, treading and numerous swimming styles, are simulated and collected. Experimental results show that we have established a prototype system which is robust and beyond the stage of proof-of-concept. Index Terms--Crowded scenario, dynamic aquatic background, foreground silhouette extraction, modeling of water crisis behaviors, real-time video surveillance system.

This paper proposes a new model for the pulsed neural network. In this model, the information is coded in terms of firing times of pulses that are generated by the neuron. The pulses transmit through the network and excite the dynamics of the neuron. Their synchronism is utilized to design the architecture of the neural network such that it acts as a radial basis function (RBF) network. A new network-learning algorithm is also developed for this pulsed RBF network. The RBF neurons are generated based on the feature of the training data, and the synaptic delays can be adjusted to distribute these RBF neurons in the training data space. The pulse neural network has been implemented compactly with multiplierless approach for both the forward computation and learning algorithm with a field programmable gate array board. As an application demonstration, it is extended to a nonlinear look-up table and applied to estimate the friction occurs in a precision linear stage. Index Terms--Field programmable gate array (FPGA), linear motors, pulsed neural network, radial basis function (RBF) neural network.

In this paper, we treat the problem of combining fingerprint and speech biometric decisions as a classifier fusion problem. By exploiting the specialist capabilities of each classifier, a combined classifier may yield results which would not be possible in a single classifier. The Feedforward Neural Network provides a natural choice for such data fusion as it has been shown to be a universal approximator. However, the training process remains much to be a trial-and-error effort since no learning algorithm can guarantee convergence to optimal solution within finite iterations. In this work, we propose a network model to generate different combinations of the hyperbolic functions to achieve some approximation and classification properties. This is to circumvent the iterative training problem as seen in neural networks learning. In many decision data fusion applications, since individual classifiers or estimators to be combined would have attained a certain level of classification or approximation accuracy, this hyperbolic functions network can be used to combine these classifiers taking their decision outputs as the inputs to the network. The proposed hyperbolic functions network model is first applied to a function approximation problem to illustrate its approximation capability. This is followed by some case studies on pattern classification problems. The model is finally applied to combine the fingerprint and speaker verification decisions which show either better or comparable results with respect to several commonly used methods. Index Terms--Biometrics, decisions fusion, fingerprint verification, multibiometrics, speaker verification.

The multivariate polynomial model provides an effective way to describe complex nonlinear input-output relationships since it is tractable for optimization, sensitivity analysis, and prediction of confidence intervals. However, for high-dimensional and high-order problems, multivariate polynomial regression becomes impractical due to its huge number of product terms. This is especially true for the case of a full interaction model. In this paper, we propose a reduced multivariate polynomial model to circumvent the dimensionality problem with some compromise in its approximation capability. In multimodal biometrics and many classifiers fusion applications, as individual classifiers to be combined would have attained a certain level of classification accuracy, this reduced multivariate polynomial model can be used to combine these classifiers in the next level of classification taking their outputs as the inputs to the reduced multivariate polynomial model. The model is first applied to a well-known pattern classification problem to illustrate its classification capability. The reduced multivariate polynomial model is then applied to combine two biometric verification systems with improved receiver operating characteristics performance as compared to an optimal weighing method and a few commonly used classifiers. Index Terms--Biometrics, classification, data fusion and multivariate polynomials, pattern recognition.

This paper presents a novel approach for face recognition based on the fusion of the appearance and depth information at the match score level. We apply passive stereoscopy instead of active range scanning as popularly used by others. We show that present-day passive stereoscopy, though less robust and accurate, does make positive contribution to face recognition. By combining the appearance and disparity in a linear fashion, we verified experimentally that the combined results are noticeably better than those for each individual modality. We also propose an original learning method, the bilateral two-dimensional linear discriminant analysis (B2DLDA), to extract facial features of the appearance and disparity images. We compare B2DLDA with some existing 2DLDA methods on both XM2VTS database and our database. The results show that the B2DLDA can achieve better results than others.