Optimizing Train-Test Data for Person Re-Identification in Real-World Applications

Abstract

Person re-identification (re-ID) aims to recognize an identity in non-overlapping camera views. Recently, re-ID received increased attention due to the growth of deep learning and its prominent applications in the field of automated video surveillance. The performance of deep learning-based methods relies heavily on the quality of training datasets and protocols. Particularly, parameters associated to the train and test set construction affect the overall performance. However, public re-ID datasets usually come with a fixed set of parameters, which are partly suitable for optimizing re-ID applications. In this paper, we study dataset construction parameters to improve re-ID performance. To this end, we first experiment on the temporal subsampling rate of the sequence of bounding boxes. Second, an experiment is performed on the effects of bounding-box enlargement under various temporal sampling rates. Thirdly, we analyze how the optimal choice of such dataset design parameters change with the dataset size. The experiments reveal that a performance increase of 2.1% Rank-1 is possible over a state-of-the-art re-ID model when optimizing the dataset construction parameters, thereby increasing the state-of-the-art performance from 91.9% to 94.0% Rank-1 on the popular DukeMTMC-reID dataset. The obtained results are not specific for the applied model and likely generalize to others.