Deep Learning Approach Protecting Privacy in Camera-Based Critical Applications
This addresses privacy concerns for users of camera-based critical applications, but appears incremental as it builds on existing saliency concepts without a clear breakthrough.
The paper tackles the problem of privacy in camera-based systems by distinguishing between salient and non-salient objects to avoid capturing unnecessary information, but no concrete results or numbers are provided.
Many critical applications rely on cameras to capture video footage for analytical purposes. This has led to concerns about these cameras accidentally capturing more information than is necessary. In this paper, we propose a deep learning approach towards protecting privacy in camera-based systems. Instead of specifying specific objects (e.g. faces) are privacy sensitive, our technique distinguishes between salient (visually prominent) and non-salient objects based on the intuition that the latter is unlikely to be needed by the application.