  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.
