Effective multi-shot person re-identification through representative frames selection and temporal feature pooling

Multi-shot person re-identification (ReID) is a popular case of person ReID in which a set of images are processed for each person. However, using entire image set for person ReID as most experimented proposals is not always effective because of time and memory consuming. The main contribution of this work is the proposed strategies for (1) choosing representative image frames for each individual instead of entire set of frames, and (2) temporal feature pooling in multi-shot person ReID. These strategies are efficiently integrated in a person ReID framework which uses GoG (Gaussian of Gaussian) and XQDA (metric learning Cross-view Quadratic Discriminant Analysis) for person representation and matching. The effectiveness of the proposed framework on two benchmark datasets (PRID 2011 and iLIDS-VID) in terms of re-identification accuracy, computational time, and storage requirements are deeply investigated and analyzed. The experimental results allow to provide several recommendations on the use of these schemes based on the characteristics of the working dataset and the requirement of the applications. Furthermore, the study also offers a desktop-based application for person search and ReID. The implementation of the proposed framework will be made publicly available.