The conventional method for moving human target detection and tracking has come across a major setback due to various hindering factors such as environmental lighting conditions, temperature, etc. Similarly, it has been noticed that the manual selection of moving human targets in a video sequence does not provide convincing results either. In this paper, a new method for moving human target detection and tracking is proposed. It involves two stages. The first stage consists in the detection of moving human targets and the second one in target tracking based on the Continuously Adaptive Mean-Shift (CAMShift) algorithm. In the first stage, in order to select the moving target, the background subtraction method and frame subtraction method are combined. The Region Of Interest (ROI), which is usually the moving target is identified. In the second stage, target tracking is performed by choosing a centroid pixel point over the ROI, which is then used by the CAMShift algorithm. The proposed method has shown outperforming results for various performance parameters such as precision, accuracy, recall, and the F1-score under three different lighting conditions. The results obtained also show a reduction in time complexity in comparison with the state-of-the-art algorithms.

Electricity is one of the essential needs of human. It's commonly used for domestic, industrial, and agricultural purposes in day-to-day life. Most folks know the role of the energy meter within the electricity grid. It’s a fundamental component of the distribution grid. The energy meter helps the utility (electricity distribution company) to account for the utilization of electricity by the buyer on a KW per hour basis. To know this we want to seek out the drawbacks of the present energy meter, and therefore the biggest problem in electricity metering. The most important problem in electricity distribution is collecting meter reading data. Straight away meter reading is collected manually, which provides scope for corruption and human error in reading. It is the wastage of manpower and utility of resources. Our existing electricity meter isn't tamper-proof. There is to several temper cases are detected. In the existing meter, there is no under voltage and over voltage protection, as well as there is no over power use alert system. Because of these problems, the utility is unable to gather a fine for max demand cross. Power theft is the biggest problem in recent days, which causes a lot of loss to electricity boards. To overcome these drawbacks, the IoT-based Smart Energy Meter (SEM) is developed.

Detecting and discriminating humans in video frames for surveillance applications is a demanding task. Identifying and highlighting humans by eliminating shadows from the video frames is vital for prudence motive. In this paper, a three-step procedure is proposed, which includes motion detection by background subtraction in live video frames, morphological gradient-based shadow removal, and human detection by Hybrid Feature Set (HFS), which comprises Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) with adaptive Neuro-Fuzzy inference system. The first step incorporates static background subtraction and dynamic background subtraction. The second step is to remove shadows by using a morphological gradient with the horizontal directional mask. The third step includes near-field, mid-field, and far-field human detection by using an adaptive Neuro-Fuzzy inference system. The results obtained from the various performed experimental analysis demonstrates diverse parametrical measures, which outperforms comparatively when benchmark databases and real-time surveillance video frames were used.

A novel prediction-based Reversible Steganographic scheme based on image inpainting can be optimized by choosing the reference pixels using optimization technique. Partial differential equations based on image inpainting are introduced to generate a prediction image, from the reference image that has similar structural and geometric information as the cover image. Then the histogram of the prediction error is shifted to embed the secret bits reversibly using two selected sets of peak points and zero points. From the stego image, the cover image can be restored losslessly after extracting the embedded bits correctly. Since the same reference pixels can be exploited in the extraction procedure, the embedded secret bits can be extracted. Through optimization of reference pixels selection and the inpainting predictor, the prediction accuracy is high, and more embeddable pixels are acquired with greater embedding rate and better visual quality, and reduced time required for the stego00 image generation.