Speaker
Description
In recent times, visual surveillance systems have undergone rapid advancements, exhibiting increased capabilities and widespread integration of artificial intelligence. Concurrently, these surveillance systems have raised concerns by exposing the public to emerging privacy and security risks. Instances of overt misuse of surveillance technologies have surged, prompting the implementation of data privacy regulations such as GDPR in Europe to establish guidelines for responsible data collection and processing.
Despite these regulatory measures, there remains a pressing need for a secure and private approach to train sophisticated machine learning and deep learning algorithms. In this paper, we propose a method that prioritizes privacy in visual surveillance. Initially, we curate a dataset consisting of videos with preserved privacy. The content within these videos is obfuscated using a combination of Gaussian Mixture Model (GMM) and selective encryption techniques. Subsequently, we employ this privacy-preserved dataset to train high-performance object detection models.
