  Privacy protection from surreptitious video recordings is an important
societal challenge. We desire a computer vision system (e.g., a robot) that can
recognize human activities and assist our daily life, yet ensure that it is not
recording video that may invade our privacy. This paper presents a fundamental
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 (LR) training 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 creating 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.
