An Efficient Person Re-Identification Method Based on Deep Transfer Learning Techniques

Person re-identification (re-id) is a significant
process in applications of video analysis. Several applications in
different areas such as airports and stations are used multiple
cameras in different places for monitoring and investigation,
which are expensive and can be easily abused. Therefore,
automatic person re-identification techniques are highly
required. The main issue of this field is to find distinguishing
features that represent the person. In this paper, we proposed
an efficient method to extract the main features based on the
deep transfer learning technique for a person re-id system. In
addition, we employed a support vector classifier (SVC) as a
separated classifier for the final decision to increase the
accuracy of the system. We employed several publicly available
datasets, which are the main datasets used for person re-id
purposes in the literature. The proposed method achieved the
best accuracy of 89.59% for rank-1, which outperforms the
state-of the-art methods. Finally, the simulation results reveal
that the proposed system is efficient prior to person re-id.