Towards effective person search with deep learning: A survey from systematic perspective
Pengcheng Zhang, Xiaohan Yu, Chen Wang, Jin Zheng, Xin Ning, Xiao Bai
Abstract
Person search detects and retrieves simultaneously a query person across uncropped scene images captured by multiple non-overlapping cameras. In light of the deep learning advancement, person search has emerged as a promising research direction that demonstrates great potential for real-world applications. This paper presents a systematic survey of deep learning methods for person search. Different from existing categorizations, we propose a new taxonomy that dissects person search models into four major components i.e. , proposal prediction, feature representation learning , training objectives, and ranking optimization. The most representative works in each component are summarized with highlighted contributions to this field. An in-depth analysis is provided upon evaluation performances of state-of-the-art person search models together with a summary of benchmark datasets. Despite that significant progress has been made to date, practical and extendable person search remains an open task. We conclude with discussions on those under-explored yet challenging datasets and learning mechanisms for real-world demands to inspire future research directions.