Vehicle Counting using Video Metadata

Abstract: The current field of object detection and image recognition is huge but not without complications. Processing large amounts of high resolution videos needs powerful hardware and also risks breaching the privacy of those who are recorded. In times of increasing demand for decentralized solutions and stricter privacy protection regulations being put in place a new approach is needed. We present an alternative to traditional object detection in video where we analyze changes to its metadata over time rather than the content of the video frames. This approach has several benefits over traditional object detection: it is incredibly fast, lightweight and protects the privacy of its subjects. We have trained and evaluated several neural network models tasked with detecting and counting vehicles in various scenes and have achieved accuracies above 90%. Finally, we take the first steps toward a decentralized solution running entirely on embedded devices.