论文笔记《End-to-End Deep Learning for Person Search》

这篇文章是香港中文大学的郑晓刚于2016提出，本文是对文中的重要工作所做的一份梳理。

论文链接
http://www.ee.cuhk.edu.hk/~xgwang/publications_topic.html#person_re-identification
代码链接
https://github.com/ShuangLI59/person_search
首次阅读2017.2.17

Abstract

Existing person re-identification (re-id) benchmarks and algorithms mainly focus on matching cropped pedestrian images between queries and candidates. However, it is different from real-world scenarios where the annotations of pedestrian bounding boxes are unavailable and the target person needs to be found from whole images. (现实场景中是不存在包围框的). To close the gap, we investigate how to localize and match query persons from the scene images without relying on the annotations of candidate boxes.(研发了一种如何去定位和匹配所查询人的方法，且该方法无需依赖于候选框)。 Instead of breaking it down into two separate tasks—pedestrian detection and person re-id, we propose an end-to-end deep learning framework to jointly handle both tasks. A random sampling softmax loss is proposed to effectively train the model under the supervision of sparse and unbalanced labels(稀疏的没有标注的目标). 另一方面是重新搜集了数据集。

Introduction