  Mining the shared features of same identity in different scene, and the
unique features of different identity in same scene, are most significant
challenges in the field of person re-identification (ReID). Online Instance
Matching (OIM) loss function and Triplet loss function are main methods for
person ReID. Unfortunately, both of them have drawbacks. OIM loss treats all
samples equally and puts no emphasis on hard samples. Triplet loss processes
batch construction in a complicated and fussy way and converges slowly. For
these problems, we propose a Triplet Online Instance Matching (TOIM) loss
function, which lays emphasis on the hard samples and improves the accuracy of
person ReID effectively. It combines the advantages of OIM loss and Triplet
loss and simplifies the process of batch construction, which leads to a more
rapid convergence. It can be trained on-line when handle the joint detection
and identification task. To validate our loss function, we collect and annotate
a large-scale benchmark dataset (UESTC-PR) based on images taken from
surveillance cameras, which contains 499 identities and 60,437 images. We
evaluated our proposed loss function on Duke, Marker-1501 and UESTC-PR using
ResNet-50, and the result shows that our proposed loss function outperforms the
baseline methods by a maximum of 21.7%, including Softmax loss, OIM loss and
Triplet loss.
