Department Publications

Human activity recognition finds many applications in areas such as surveillance, and sports. Such a system classifies a spatio-temporal feature descriptor of a human figure in a video, based on training examples. However many classifiers face the constraints of the long training time, and the large size of the feature vector. Our method, due to the use of an Support Vector Machine (SVM) classifier, on an existing spatio-temporal feature descriptor resolves these problems in human activity recognition. Comparison of our system with existing classifiers using two standard datasets shows that our system is much superior in terms of the computational time, and either it surpasses or is on par with the existing recognition rates. It performs on par or marginally inferior to existing systems, when the number of training examples are a few due to the imbalance, although consistently better in terms of computation time.

The short-term electricity demand forecasting has become one of the major research area in power system engineering. By combining the smart metering to the short-term demand forecasting techniques, new features can be added to save on demand and electricity bill. This paper illustrates the methodology used to forecast electricity demand over short period of time which can be used with smart meters. Polynomial fitting with interpolation is used to forecast the demand by taking the apparent power sample points from smart meters. The outcome of this work will be beneficial to the residential or industrial electricity consumers to control the demand side loads. It will help the industrial consumers to save on maximum demand charge with the introduction of warning message or residential consumers to reduce their electricity bill by cutting down non-essential loads in peak hours.