Privacy preserved video summarization of road traffic events for IoT smart cities
Asghar, Mamoona Naveed
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The purpose of smart surveillance systems for automatic detection of road traffic accidents is to quickly respond to minimize human and financial losses in smart cities. However, along with the self-evident benefits of surveillance applications, privacy protection remains crucial under any circumstances. Hence, to ensure the privacy of sensitive data, European General Data Protection Regulation (EU-GDPR) has come into force. EU-GDPR suggests data minimisation and data protection by design for data collection and storage. Therefore, for a privacy-aware surveillance system, this paper targets the identification of two areas of concern: (1) detection of road traffic events (accidents), and (2) privacy preserved video summarization for the detected events in the surveillance videos. The focus of this research is to categorise the traffic events for summarization of the video content, therefore, a state-of-the-art object detection algorithm, i.e., You Only Look Once (YOLOv5), has been employed. YOLOv5 is trained using a customised synthetic dataset of 600 annotated accident and non-accident video frames. Privacy preservation is achieved in two steps, firstly, a synthetic dataset is used for training and validation purposes, while, testing is performed on real-time data with an accuracy from 55% to 85%. Secondly, the real-time summarized videos (reduced video duration to 42.97% on average) are extracted and stored in an encrypted format to avoid un-trusted access to sensitive event-based data. Fernet, a symmetric encryption algorithm is applied to the summarized videos along with Diffie–Hellman (DH) key exchange algorithm and SHA256 hash algorithm. The encryption key is deleted immediately after the encryption process, and the decryption key is generated at the system of authorised stakeholders, which prevents the key from a man-in-the-middle (MITM) attack.
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