Real-time monitoring of community mobility with deep learning based methods for smart campuses

Various problems come to the fore due to the fact that urban environments and therefore campus environments are hosting more and more dense crowds of people. The first thing to do to solve these problems is; These crowds need to be identified and analyzed. In this direction, in this thesis, it is aimed to determine and examine the student crowds in the university campus. For this purpose, object detection was made using deep learning methods in order to detect people in the images. The commonly used datasets in the literature were examined and it was seen that the CrowdHuman dataset was the most appropriate dataset. There are 15 000 images in the relevant data set and approximately 470 000 tagged human data. State-of-the-art models such as Faster RCNN, YOLO, and RetinaNet were used in the trainings. In the experiments, 78% mAP score was obtained with YOLOv4. The models trained here were run on the server, and real-time object detection was made on the images obtained from the IP cameras in the campus. With the detection of human density in the image, the density status of the relevant region is shown on the mobile application.