GAN-based person search via deep complementary classifier with center-constrained Triplet loss

Highlights：

• We propose a framework for person search that utilizes GAN-generated images in a novel deep network, which solves the typical problem of low numbers of samples in big data and can adapt to images of various types of pedestrians.

• We propose a deep complementary classifier for pedestrian detection to leverage complementary object regions for pedestrian/non-pedestrian classification that can improve the overall performance of the person search model.

• We propose a new loss function, named the center-constrained triplet loss, that combines the advantages of both center loss and triplet loss to minimize intraperson variations and maximize interperson variations. In addition, the center-constrained triplet loss can avoid the disadvan- tages of the triplet loss’s careful selection of the required triplets for training.

• We conduct experiments on the large-scale CUHK-SYSU and PRW datasets and find that a newly synthesized image from the original image helps improve the performance of the model. Our proposed loss achieves significant improvements over the compared approach in both mAP and top-1 evaluation protocols.