PrivHAR: Recognizing Human Actions From Privacy-preserving Lens

Autor:	Hinojosa, Carlos, Marquez, Miguel, Arguello, Henry, Adeli, Ehsan, Fei-Fei, Li, Niebles, Juan Carlos
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Cryptography and Security Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing
Zdroj:	Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part IV
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-031-19772-7_19
Popis:	The accelerated use of digital cameras prompts an increasing concern about privacy and security, particularly in applications such as action recognition. In this paper, we propose an optimizing framework to provide robust visual privacy protection along the human action recognition pipeline. Our framework parameterizes the camera lens to successfully degrade the quality of the videos to inhibit privacy attributes and protect against adversarial attacks while maintaining relevant features for activity recognition. We validate our approach with extensive simulations and hardware experiments. Comment: Oral paper presented at European Conference on Computer Vision (ECCV) 2022, in Tel Aviv, Israel
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2206.03891 Zobrazit plný text záznamu View this record from Arxiv