An Encoder-Decoder based Deep Learning Approach for Anonymization of Visual Surveillance Media with Preservation of Utility

2020

Master's thesis in Information- and communication technology (IKT590) The field of computer vision has seen significant progress recently fol-lowing innovations in deep learning neural networks. Activity can be identified from surveillance cameras. Automatic detection of unwanted incidents would enable police to act quickly with appropriate resources. Activity detecting machine learning algorithms need many examples in its learning phase. However, videos from surveillance cameras may contain privacy-sensitive information. The videos are not allowed to be used outside of the police unless anonymized. However, traditional anonymization techniques remove visual information, reducing utility as…