Person Search Challenges and Solutions: A Survey

Abstract

Person search has drawn increasing attention due to its real-world applications and research significance. Person search aims to find a probe person in a gallery of scene images with a wide range of applications, such as criminals search, multicamera tracking, missing person search, etc. Early person search works focused on image-based person search, which uses person image as the search query. Text-based person search is another major person search category that uses free-form natural language as the search query. Person search is challenging, and corresponding solutions are diverse and complex. Therefore, systematic surveys on this topic are essential. This paper surveyed the recent works on image-based and text-based person search from the perspective of challenges and solutions. Specifically, we provide a brief analysis of highly influential person search methods considering the three significant challenges: the discriminative person features, the query-person gap, and the detection-identification inconsistency. We summarise and compare evaluation results. Finally, we discuss open issues and some promising future research directions.

arXiv comment8 pages; Accepted by IJCAI 2021 Survey Track

References in corpus (7)

• Distilling the Knowledge in a Neural Network
• Identity-Aware Textual-Visual Matching with Latent Co-attention
• ViTAA: Visual-Textual Attributes Alignment in Person Search by Natural Language
• Knowledge Distillation for End-to-End Person Search
• End-to-end Person Search Sequentially Trained on Aggregated Dataset
• Robust Partial Matching for Person Search in the Wild
• Segmentation Mask Guided End-to-End Person Search