This survey paper focused on data mining technique based on artificial neural network and its application in runoff forecasting. The long-term and short-term forecasting model was developed for runoff forecasting using various approaches of Artificial Neural Network techniques. This study compares various approaches available for runoff forecasting of artificial neural networks (ANNs). On the basis of this comparative study, it is tried to find out better approach in perspective of research work.

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We present a novel, real-time, markerless vision-based tracking system, employing a rigid orthogonal configuration of two pairs of opposing cameras. Our system uses optical flow over sparse features to overcome the limitation of vision-based systems that require markers or a pre-loaded model of the physical environment. We show how opposing cameras enable cancellation of common components of optical flow leading to an efficient tracking algorithm. Experiments comparing our device with an electromagnetic tracker show that its average tracking accuracy is 80% over 185 frames, and it is able to track large range motions even in outdoor settings.

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