A multi-task learning based hybrid prediction algorithm for privacy preserving human activity recognition framework

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.

Discover human poses similarity and action recognition based on machine learning

“…Pose estimation uses key points and body joints, such as the elbows and wrists, to locate and track human posture automatically, in addition to determining the orientation of body limbs [6]. This task has a lot of potential in many applications, which include tracking human body movement or analyzing and detecting inappropriate human behavior [7]- [9]. Pose estimation is used in sports performance analysis to track and evaluate human movement accuracy, as well as in a variety of other fields like human-computer interaction (HCI) and augmented reality (AR).…”

Attention correlated appearance and motion feature followed temporal learning for activity recognition

“…Particularly, multiple images are commonly adopted as an input vector which has the embedded temporal information as well as the spatial information [2], [4]. In addition to improving learning, many researchers used temporal networks to perform large-scale visual learning and activity classification from video clips, where temporal networks had recurrent connections to aid in video context understanding regarding time [2], [4]- [7].…”