Generalizable Person Search on Open-world User-Generated Video Content
Junjie Li Guanshuo Wang Yichao Yan Fufu Yu Qiong Jia Jie Qin Shouhong Ding Xiaokang Yang
Person search is a challenging task that involves detecting and retrieving
individuals from a large set of un-cropped scene images. Existing person search
applications are mostly trained and deployed in the same-origin scenarios.
However, collecting and annotating training samples for each scene is often
difficult due to the limitation of resources and the labor cost. Moreover,
large-scale intra-domain data for training are generally not legally available
for common developers, due to the regulation of privacy and public security.
Leveraging easily accessible large-scale User Generated Video Contents
(\emph{i.e.} UGC videos) to train person search models can fit the open-world
distribution, but still suffering a performance gap from the domain difference
to surveillance scenes. In this work, we explore enhancing the out-of-domain
generalization capabilities of person search models, and propose a
generalizable framework on both feature-level and data-level generalization to
facilitate downstream tasks in arbitrary scenarios. Specifically, we focus on
learning domain-invariant representations for both detection and ReID by
introducing a multi-task prototype-based domain-specific batch normalization,
and a channel-wise ID-relevant feature decorrelation strategy. We also identify
and address typical sources of noise in open-world training frames, including
inaccurate bounding boxes, the omission of identity labels, and the absence of
cross-camera data. Our framework achieves promising performance on two
challenging person search benchmarks without using any human annotation or
samples from the target domain.