Inter-Camera Tracking Based on Fully Unsupervised Online Learning
Abstract
In this paper, we present a novel fully automatic approach to track the same human across multiple disjoint cameras. Our framework includes a two-phase feature extractor and an online-learning-based camera link model estimation. We introduce an effective and robust integration of appearance and context features. Couples are detected automatically, and the couple feature is also integrated with appearance features effectively. The proposed algorithm is scalable with the use of a fully unsupervised online learning framework. In the experiments, it outperforms all the state-of-the-art methods on the benchmark NLPR_MCT dataset.
Citation
@inproceedings{Lee17UnsupervisedICT,
author = {Young-Gun Lee and Zheng Tang and Jenq-Neng Hwang and Zhijun Fang},
title = {Inter-camera tracking based on fully unsupervised online learning},
booktitle = {Proc. ICIP},
pages = {2607–2611},
address = {Beijing, China},
year = {2017}
}