Authors: Tong Xiao,Shuang Li,Bochao Wang,Liang Lin,Xiaogang Wang
Where published:
CVPR 2017 7
ArXiv: 1604.01850
Document:
PDF
DOI
Artifact development version:
GitHub
Abstract URL: http://arxiv.org/abs/1604.01850v3
Existing person re-identification benchmarks and methods mainly focus on
matching cropped pedestrian images between queries and candidates. However, it
is different from real-world scenarios where the annotations of pedestrian
bounding boxes are unavailable and the target person needs to be searched from
a gallery of whole scene images. To close the gap, we propose a new deep
learning framework for person search. Instead of breaking it down into two
separate tasks---pedestrian detection and person re-identification, we jointly
handle both aspects in a single convolutional neural network. An Online
Instance Matching (OIM) loss function is proposed to train the network
effectively, which is scalable to datasets with numerous identities. To
validate our approach, we collect and annotate a large-scale benchmark dataset
for person search. It contains 18,184 images, 8,432 identities, and 96,143
pedestrian bounding boxes. Experiments show that our framework outperforms
other separate approaches, and the proposed OIM loss function converges much
faster and better than the conventional Softmax loss.