Population Density Estimation using Single Shot Detection Algorithm

The proposed system consists of an Artificial Intelligent software that is capable of counting the number of people from the user inputs such as images and video frames more accurately. The system uses the advanced MobileNet Single Shot Detection algorithm to detect the human class in the given frame at the given moment. The count of individuals in the frame is dependent on the threshold set by the end-user. The primary goal of the application is to provide an easy-to-use and efficient tool for analyzing images and videos. The model used for object detection is trained on the mall dataset from Kaggle website and is capable of accurately detecting people in real-time, making it suitable for a wide range of applications. The people in the frame are tracked using centroid tracker algorithm, and the tracked classes are counted. The detection algorithm is built using the popular library keras for its versatile usage. This system uses Keras, a high-level deep learning library, and OpenCV, a computer vision library, to perform real-time people counting. The system utilizes object detection algorithms to detect and track individuals, and then increments a count each time a person crosses a designated line. The system is capable of handling multiple people simultaneously and accurately counting them in real-time.