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 tracking-by-detection structure applied in offline pixelation 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 as the backbone, 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, our PsOP unifies the pixelation of discriminate and indiscriminate pixelation objects through trajectories generation. In addition to the pixelation accuracy boosting, experiment results on the streaming video data we built show that the proposed PsOP can significantly reduce the overpixelation ratio and the human intervention in privacy-sensitive object pixelation.