Abstract. Privacy protection from video taken by wearable cameras is an important societal challenge. We desire a wearable vision system that can recognize human activities, yet not disclose the identity of the participants. Video anonymization is typically handled by decimating the image to a very low resolution. Activity recognition, however, generally requires resolution high enough that features such as faces are identifiable. In this paper, we propose a new approach to address such contradicting objectives: human activity recognition while only using extreme low-resolution (e.g., 16x12) anonymized videos. We introduce the paradigm of inverse super resolution (ISR), the concept of learning the optimal set of image transformations to generate multiple low-resolution videos from a single video. Our ISR learns different types of sub-pixel transformations optimized for the activity classification, allowing the classifier to best take advantage of existing high-resolution videos (e.g., YouTube videos) by generating multiple LR training videos tailored for the problem. We experimentally confirm that the paradigm of inverse super resolution is able to benefit activity recognition from extreme low-resolution videos (e.g., 16x12 and 32x24), particularly in first-person scenarios.
Ryoo et al. REF worked on learning of efficient low resolution video transforms to classify actions from extreme low resolution videos.
Privacy-Preserving Egocentric Activity Recognition from Extreme Low Resolution
ArXiv
ArXiv
Computer Science