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Electricity conservation techniques have gained more importance in recent years. Many smart techniques are invented to save electricity with the help of assisted devices like sensors. Though it saves electricity, it adds an additional sensor cost to the system. This work aims to develop a system that manages the electric power supply, only when it is actually needed i.e., the system enables the power supply when a human is present in the location and disables it otherwise. The system avoids any additional costs by using the closed circuit television, which is installed in most of the places for security reasons. Human detection is done by a Modified-single shot detection with a specific hyperparameter tuning method. Further the model is pruned to reduce the computational cost of the framework which in turn reduces the processing speed of the network drastically. The model yields the output to the Arduino micro-controller to enable the power supply in and around the location only when a human is detected and disables it when the human exits. The model is evaluated on CHOKEPOINT dataset and real-time video surveillance footage. Experimental results have shown an average accuracy of 85.82% with 2.1 seconds of processing time per frame.

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The demand for electrical energy in developing countries is apparently increasing thereby creating a large gap between the availability of the electrical resource and its growing demand. Globally reputed energy economists have recognized that 25% of reduction in energy consumption can be achieved by adopting efficient energy conservation techniques This paper presents one of the simplest ways of conservation techniques that enables the electric power supply only when it is actually needed. It is an automatic system that functions with the existing CCTV surveillance camera to enable/disable the electric power supply, only in the location where human is present / absent respectively. The proposed approach is demonstrated without the use of sensors, based on Regional Convolutional Neural Network (R-CNN). A new R-CNN model is constructed for CHOKEPOINT dataset and the optimization is done using Nadam technique. The results are then fed into Arduino micro controller to control the electric supply based on the presence/absence of human in the particular region.

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