Person search has recently been a challenging task in the computer vision
domain, which aims to search specific pedestrians from real
cameras.Nevertheless, most surveillance videos comprise only a handful of
images of each pedestrian, which often feature identical backgrounds and
clothing. Hence, it is difficult to learn more discriminative features for
person search in real scenes. To tackle this challenge, we draw on Generative
Adversarial Networks (GAN) to synthesize data from surveillance videos. GAN has
thrived in computer vision problems because it produces high-quality images
efficiently. We merely alter the popular Fast R-CNN model, which is capable of
processing videos and yielding accurate detection outcomes. In order to
appropriately relieve the pressure brought by the two-stage model, we design an
Assisted-Identity Query Module (AIDQ) to provide positive images for the behind
part. Besides, the proposed novel GAN-based Scene Synthesis model that can
synthesize high-quality cross-id person images for person search tasks. In
order to facilitate the feature learning of the GAN-based Scene Synthesis
model, we adopt an online learning strategy that collaboratively learns the
synthesized images and original images. Extensive experiments on two widely
used person search benchmarks, CUHK-SYSU and PRW, have shown that our method
has achieved great performance, and the extensive ablation study further
justifies our GAN-synthetic data can effectively increase the variability of
the datasets and be more realistic.

通过使用生成对抗网络，本研究设计了一种生成高质量跨身份场景合成的模型，用于人物搜索任务，实验证明该方法能够有效增加数据集的多样性和真实性。