Pixelation is NOT Done in Videos Yet
This paper introduces an algorithm to protect the privacy of individuals in streaming video data by blurring faces such that face cannot be reliably recognized. This thwarts any possible face recognition, but because all facial details are obscured, the result is of limited use. We propose a new clustering algorithm to create raw trajectories for detected faces. Associating faces across frames to form trajectories, it auto-generates cluster number and discovers new clusters through deep feature and position aggregated affinities. We introduce a Gaussian Process to refine the raw trajectories. We conducted an online experiment with 47 participants to evaluate the effectiveness of face blurring compared to the original photo (as-is), and users' experience (satisfaction, information sufficiency, enjoyment, social presence, and filter likeability)
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringFace DetectionFace RecognitionPositionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Privacy-sensitive Objects Pixelation for Live Video Streaming
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 tra…
ClusteringPersonal Privacy Protection via Irrelevant Faces Tracking and Pixelation in Video Live Streaming
To date, the privacy-protection intended pixelation tasks are still labor-intensive and yet to be studied. With the prevailing of video live streaming, establishing an online face pixelation mechanism during streaming is…
ClusteringFace DetectionFAKER: Full-body Anonymization with Human Keypoint Extraction for Real-time Video Deidentification
In the contemporary digital era, protection of personal information has become a paramount issue. The exponential growth of the media industry has heightened concerns regarding the anonymization of individuals captured i…
Full-body anonymizationPose EstimationRotation Equivariant Graph Convolutional Network for Spherical Image Classification
Convolutional neural networks (CNNs) designed for low-dimensional regular grids will unfortunately lead to non-optimal solutions for analyzing spherical images, due to their different geometrical properties from planar i…
ClassificationGeneral Classificationgraph constructionimage-classification+1Lester: rotoscope animation through video object segmentation and tracking
This article introduces Lester, a novel method to automatically synthetise retro-style 2D animations from videos. The method approaches the challenge mainly as an object segmentation and tracking problem. Video frames ar…
3D Human Pose EstimationObjectPose EstimationSemantic Segmentation+3