Platform Construction to Protect Portraits of Others

As the times change, online video activities and video sharing have become commonplace. However, publicly sharing the images of others can involve risks of violating privacy and portrait rights, especially in today's era of widespread high-resolution cameras that can capture detailed facial features. Therefore, protecting the portrait rights of others has become crucial. In order to balance the protection of portrait and the preservation of image quality, we propose a deep learning-based person tracking system. This system can record and track the time and location of people in videos or video streams. Through image synthesis techniques, we can either place the necessary people in virtual environments or hide people in real videos. In this way, we ensure the portrait and privacy of others while maintaining the authenticity and quality of the image. This approach is not only applicable for individuals protecting their own images but can also be applied to respect the privacy of others, making online video activities safer and more respectful of people rights.