Person search: New paradigm of person re-identification: A survey and outlook of recent works

Abstract

Person Search (PS) has become a major field because of its need in community and in the field of research among researchers. This task aims to find a probe person from whole scene which shows great significance in video surveillance field to track lost people, re-identification, and verification of person. In last few years, deep learning has played unremarkable role for the solution of re-identification problem. Deep learning shows incredible performance in person (re-ID) and search. Researchers experience more flexibility in proposing new methods and solve challenging issues such as low resolution, pose variation, background clutter, occlusion, viewpoints, and low illumination. Specially, convolutional neural network (CNN) achieves breakthrough performance and extracts useful patterns and characteristics. Development of new framework takes substantial efforts; hard work and computation cost are required to acquire excellent results. This survey paper includes brief discussion about feature representation learning and deep metric learning with novel loss functions. We thoroughly review datasets with performance analysis on existing datasets. Finally, we are reviewing current solutions for further consideration.

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