Distance-based camera network topology inference for person re-identification

Abstract

In this study, we propose a novel distance-based camera network topology inference method for efficient person re-identification. We first calibrate each camera, and estimate the relative scales between cameras. Using the calibration results for multiple cameras, we estimate the speed of each person, and infer the distances between cameras to generate a distance-based camera network topology. The proposed distance-based topology can be adaptively applied to each person according to their speed, and can handle diverse transition times of people between non-overlapping cameras. To validate the proposed method, we test it using open person re-identification datasets, and compare the results with state-of-the-art methods. The experimental results demonstrate that the proposed method is effective for person re-identification in a large-scale camera network with various transition times for people.