Gunawath Mudiyanselage Roshan Indika Godaliyadda

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This paper addresses the specific problem of human event detection from a video sequence in both indoor and outdoor environments. Foreground image pixels are identified through the principle of background subtraction by defining a reference background model using a mixture of time varying Gaussian distributions. Color filtering in the RGB space is then used(More)
This paper presents a subspace signature based approach for the identification of turned on appliances at a given observation time using one single-function smart meter. The novelty of the proposed approach compared to existing method is its capability for proper identification while relying on a significantly lower amount of measurement data. Unlike(More)
A real-time event tracking method is proposed that is immune to background variances. The proposed method models each pixel as a collection of Gaussian distributions to handle background variations and uses manipulations in the RGB space to mitigate the effects of foreground shadows. A two stepped connected component analysis method is also introduced in(More)
This paper tackles the challenging problem of accurate indoor geolocation for UWB systems in hazardous multipath environments through three versatile super resolution techniques: time domain MUSIC (Multiple Signal Classification), frequency domain MUSIC algorithms and frequency domain EV (Eigen Value) method. The resultant pseudo-spectrums generated by(More)
Extraction of unknown independent source signals from a noisy mixture is a fundamental problem in most signal processing applications. The existing independent component analysis (ICA) algorithms have tackled this problem for complex and real valued mixtures for both super and sub Gaussian sources. However in reality super and sub Gaussian sources exist(More)
The performance of interference cancellation systems based on Wiener filters relies on proper modeling of the channel between the adaptive filter input and the reference signal. Once the optimal condition is achieved the correlated interference signal components are cancelled out and the desired signal can be extracted as the Wiener filter error signal. In(More)
This paper addresses the generic object counting problem with object overlapping occurring at varied levels and degrees. The overall image containing the objects is segmented from the background. Thereafter a combination of parameters is extracted from each of the segments to construct a parameter space. The overall space formed by these vectors contains(More)
Obstacle detection and map generation is an essential tool for site reconnaissance applications. Further it enables optimal and efficient path planning for mobile agents to navigate in unknown environments. This paper proposes a solution to this problem through a stereo vision-based obstacle detection and depth measurement method for reconnaissance. The(More)
A novel method of modelling pixel distributions for foreground detection in rapidly fluctuating dynamic background conditions is presented in this paper. A comprehensive study of the characteristics of pixel behaviour in videos of backgrounds in clear water under natural lighting conditions has been presented in this work. Videos from real world situations(More)
This paper proposes a subspace based classifier, which can separate highly correlated acoustic signals based on source material. In this method, the optimum set of Eigen-filters that form the subspace classifier are selected such that the cross correlation between different classes is minimized. The proposed method has high noise immunity as the noise(More)