# Privacy-Preserving Human Activity Recognition from Extreme Low Resolution

arXiv: https://arxiv.org/abs/1604.03196 · HTML: https://mlsanity.com/paper/1604.03196/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2016.

## Built on by (most cited first, 30 of 43)

- [An Outlook into the Future of Egocentric Vision](https://mlsanity.com/paper/2308.07123/) (2023)
- [A Study of Face Obfuscation in ImageNet](https://mlsanity.com/paper/2103.06191/) (2021)
- [SPAct: Self-supervised Privacy Preservation for Action Recognition](https://mlsanity.com/paper/2203.15205/) (2022)
- [STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition](https://mlsanity.com/paper/2301.03046/) (2023)
- [IdentityDP: Differential Private Identification Protection for Face Images](https://mlsanity.com/paper/2103.01745/) (2021)
- [TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection](https://mlsanity.com/paper/2308.11072/) (2023)
- [Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition](https://mlsanity.com/paper/2306.11546/) (2023)
- [Instance Tracking in 3D Scenes from Egocentric Videos](https://mlsanity.com/paper/2312.04117/) (2023)
- [PrivHAR: Recognizing Human Actions From Privacy-preserving Lens](https://mlsanity.com/paper/2206.03891/) (2022)
- [Generative Adversarial Networks: A Survey Towards Private and Secure Applications](https://mlsanity.com/paper/2106.03785/) (2021)
- [Privacy-Preserving Action Recognition via Motion Difference Quantization](https://mlsanity.com/paper/2208.02459/) (2022)
- [Privacy-Preserving Video Classification with Convolutional Neural Networks](https://mlsanity.com/paper/2102.03513/) (2021)
- [Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors](https://mlsanity.com/paper/2105.03812/) (2021)
- [Differentially Private Video Activity Recognition](https://mlsanity.com/paper/2306.15742/) (2023)
- [Selective, Interpretable, and Motion Consistent Privacy Attribute Obfuscation for Action Recognition](https://mlsanity.com/paper/2403.12710/) (2024)
- [When Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing System](https://mlsanity.com/paper/2102.10109/) (2021)
- [Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room](https://mlsanity.com/paper/2108.11801/) (2021)
- [Privacy-Preserving Person Detection Using Low-Resolution Infrared Cameras](https://mlsanity.com/paper/2209.11335/) (2022)
- [Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images](https://mlsanity.com/paper/2303.10435/) (2023)
- [Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation](https://mlsanity.com/paper/2107.02718/) (2021)
- [3D-Aware Adversarial Makeup Generation for Facial Privacy Protection](https://mlsanity.com/paper/2306.14640/) (2023)
- [An Unsupervised Approach for Periodic Source Detection in Time Series](https://mlsanity.com/paper/2406.00566/) (2024)
- [Optical Flow for Video Super-Resolution: A Survey](https://mlsanity.com/paper/2203.10462/) (2022)
- [Privacy-preserving Optics for Enhancing Protection in Face De-identification](https://mlsanity.com/paper/2404.00777/) (2024)
- [Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization](https://mlsanity.com/paper/2504.14301/) (2025)
- [EgoPrivacy: What Your First-Person Camera Says About You?](https://mlsanity.com/paper/2506.12258/) (2025)
- [Privacy-Enhancing Optical Embeddings for Lensless Classification](https://mlsanity.com/paper/2211.12864/) (2022)
- [Video-DPRP: A Differentially Private Approach for Visual Privacy-Preserving Video Human Activity Recognition](https://mlsanity.com/paper/2503.02132/) (2025)
- [Sparse and Privacy-enhanced Representation for Human Pose Estimation](https://mlsanity.com/paper/2309.09515/) (2023)
- [CamPro: Camera-based Anti-Facial Recognition](https://mlsanity.com/paper/2401.00151/) (2023)

## Abstract (arXiv)

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.