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Harikiran Jonnadula
VIT-AP University
Electrical & Electronics Engineering

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A multi-task learning based hybrid prediction algorithm for privacy preserving human activity recognition framework Vijaya Kumar Kambala; Harikiran Jonnadula
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3204

Abstract

There is ever increasing need to use computer vision devices to capture videos as part of many real-world applications. However, invading privacy of people is the cause of concern. There is need for protecting privacy of people while videos are used purposefully based on objective functions. One such use case is human activity recognition without disclosing human identity. In this paper, we proposed a multi-task learning based hybrid prediction algorithm (MTL-HPA) towards realising privacy preserving human activity recognition framework (PPHARF). It serves the purpose by recognizing human activities from videos while preserving identity of humans present in the multimedia object. Face of any person in the video is anonymized to preserve privacy while the actions of the person are exposed to get them extracted. Without losing utility of human activity recognition, anonymization is achieved. Humans and face detection methods file to reveal identity of the persons in video. We experimentally confirm with joint-annotated human motion data base (JHMDB) and daily action localization in YouTube (DALY) datasets that the framework recognises human activities and ensures non-disclosure of privacy information. Our approach is better than many traditional anonymization techniques such as noise adding, blurring, and masking.
Co-Authors Vijaya Kumar Kambala