Deep learning for object detection in video streams

Abstract

”Artificial intelligence is no match for natural stupidity” is the famous quote of the brilliant Albert Einstein. Born in the fifties, artificial intelligence was already a source of admiration. It has undergone a spectacular evolution in recent years and continues to develop following technological and computer advances. Nowadays, artificial intelligence plays a fundamental role in research and is ubiquitous in our daily lives. The detection of objects in a video stream with deep learning having become accessible and efficient thanks to the more and more performant processor, this principle will be adapted in order to be able to use it as a door opening sensor. This thesis was conducted in a sensor company called BEA. It is a leading manufacturer of sensing solutions for automatic doors systems. The company was founded in 1965 and its headquarters are located in Li`ege, Belgium. As artificial intelligence is making great strides in our technologies, the company has decided to do some experimentations with this process. The detection and classification of objects in a video stream through deep learning is the main subject of this work. The use of it in real time is a big challenge because it was necessary to handle the use of a neural network accelerator while ensuring that the model used was not too consequent. Despite this constraint, it was still necessary that the precision and the accuracy were sufficient for the application. In order to be independent of the position of the camera and its orientation for future applications, a ground projection algorithm has been implemented. Then, to improve the feasibility of the process, a Kalman filter will be integrated into the detected objects and a tracking will be assigned to it. Subsequently, open decision-making and people counting were implemented and all was adapted to the Raspberry Pi 3 B + embedded system. Finally, various evaluation tests were carried out to demonstrate the system’s fidelity and its promising potential.