# Privacy-sensitive Objects Pixelation for Live Video Streaming

arXiv: https://arxiv.org/abs/2101.00604 · HTML: https://mlsanity.com/paper/2101.00604/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 3 Jan 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [ECO: Efficient Convolution Operators for Tracking](https://mlsanity.com/paper/1611.09224/) (2016)
- [Beyond Short Snippets: Deep Networks for Video Classification](https://mlsanity.com/paper/1503.08909/) (2015)
- [TextBoxes: A Fast Text Detector with a Single Deep Neural Network](https://mlsanity.com/paper/1611.06779/) (2016)
- [POI: Multiple Object Tracking with High Performance Detection and Appearance Feature](https://mlsanity.com/paper/1610.06136/) (2016)
- [Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking](https://mlsanity.com/paper/1803.08679/) (2018)
- [PyramidBox: A Context-assisted Single Shot Face Detector](https://mlsanity.com/paper/1803.07737/) (2018)
- [End-to-end Flow Correlation Tracking with Spatial-temporal Attention](https://mlsanity.com/paper/1711.01124/) (2017)
- [A Large-scale Dataset and Benchmark for Similar Trademark Retrieval](https://mlsanity.com/paper/1701.05766/) (2017)

## Built on by (most cited first, 5 of 5)

- [Pre-training-free Image Manipulation Localization through Non-Mutually Exclusive Contrastive Learning](https://mlsanity.com/paper/2309.14900/) (2023)
- [LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models](https://mlsanity.com/paper/2502.03950/) (2025)
- [Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoning](https://mlsanity.com/paper/2406.12736/) (2024)
- [SHAN: Object-Level Privacy Detection via Inference on Scene Heterogeneous Graph](https://mlsanity.com/paper/2403.09172/) (2024)
- [Privacy-Preserving Action Recognition: Taxonomy, Methods, and Privacy-Utility Trade-offs](https://mlsanity.com/paper/2608.04501/) (2026)

## Abstract (arXiv)

With the prevailing of live video streaming, establishing an online pixelation method for privacy-sensitive objects is an urgency. Caused by the inaccurate detection of privacy-sensitive objects, simply migrating the tracking-by-detection structure into the online form will incur problems in target initialization, drifting, and over-pixelation. To cope with the inevitable but impacting detection issue, we propose a novel Privacy-sensitive Objects Pixelation (PsOP) framework for automatic personal privacy filtering during live video streaming. Leveraging pre-trained detection networks, our PsOP is extendable to any potential privacy-sensitive objects pixelation. Employing the embedding networks and the proposed Positioned Incremental Affinity Propagation (PIAP) clustering algorithm as the backbone, our PsOP unifies the pixelation of discriminating and indiscriminating pixelation objects through trajectories generation. In addition to the pixelation accuracy boosting, experiments on the streaming video data we built show that the proposed PsOP can significantly reduce the over-pixelation ratio in privacy-sensitive object pixelation.