Precise Detection of People Using Pre-Trained Machine Learning Models With Assisting Heat Sensor Camera
Abstract: The thesis examines the possibility of developing a system that can be used to detect the number of people in a given area with high accuracy. By using a variety of hardware components, publicly available object detection models and a cloud platform a highly modern approach is examined. The aim is to develop a cost-effective and scalable system which should be able to adapt to a given area. Different modern object detection models are being evaluated based on selected metrics to optimize the outcome in the given area. The thesis is based on previous research and work in the field. The approach is based on collecting data locally using an edge device and forward relevant data to the cloud for further analysis. By enabling an interchangeable model for object detection, publicly available object detection models can be reused to evaluate its performance in a given area. External thermal data is used to validate a detection to achieve a more accurate detection, thereby extending the scope of the system. The proof of concept intends to demonstrate that the system described can be developed and evaluated with limited financial resources. Further analysis is intended to reveal further uses of the system, but since this is not the primary objective, it will be discussed in a purely abstract manner.
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