Abstract: The aim of project is to automatically estimate the number of people at indoor and outdoor places. People counting systems can be used in retail environment such as determining conversion ratio, advertising and promotional evaluation. This system can be used for transportation management system and video surveillance. The number of customers is indispensable data for management and decision making in public places like large-scale markets, shopping centers, airports, stations, museums, laboratories, classrooms, cafeteria etc. In this system, firstly extract the frames from the video, then draw a desired reference line on the input frame, detect the people using MobileNet-SSD object detection model, mark the centroid on the detected person, track the movement of that marked centroid and calculate the direction of centroid movement whether it is moving upwards or downwards. If the centroid movement is downward direction, then increment in counter, else if the centroid movement is upward direction, then increment out counter. People counting is essential for retailers of any size, but it's especially important for small businesses that don’t have the benefit of assaying data from multitudinous locales whenmaking pivotal opinions. When used intelligently, people counting can shape businesses in multitudinous ways other than just furnishing information on nethermost business.
Abstract: One’s mental health instability can hinder the individual’s life that leads to various health issues, like depression and anxiety that in turn results in mental imbalance or severe psychological instability. This psychological instability can lead to bipolar disorder. There are various reasons affecting one’s mental well-being, the reasons can either be modifiable or nonmodifiable. Bipolar disorder causes changes in a person's mood and energy. People will experience intense emotional states because of disorder. Proper diagnosis and treatment is required for the people with this disorder which lead to healthy and active lives. Determination of this psychological instability can be predicted using machine learning and deep learning algorithms and the accuracies will be compared for the same. The dataset used is a survey based real time dataset which identifies the everyday activities and conditions of various individuals. The survey questionnaire consists of various questions determining the stress and psychological feelings among the individuals. This dataset is used in training the models to determine the prevalence of any psychological instability. Comparison of various bipolar classification methods with their performance accuracy against the real- time dataset is done. Detection of psychological instability plays a key role in reducing the risk of severity
