Person Search
47 papers with code • 2 benchmarks • 9 datasets
Person Search is a task which aims at matching a specific person among a great number of whole scene images.
Source: Re-ID Driven Localization Refinement for Person Search
Datasets
Most implemented papers
Segmentation Mask Guided End-to-End Person Search
Person search aims to search for a target person among multiple images recorded by multiple surveillance cameras, which faces various challenges from both pedestrian detection and person re-identification.
Knowledge Distillation for End-to-End Person Search
We employ this to supervise the detector of our person search model at various levels using a specialized detector.
Color inference from semantic labeling for person search in videos
We propose a method based on binary search trees and a large peer-labelled color name dataset.
Interactive Natural Language-based Person Search
In this work, we consider the problem of searching people in an unconstrained environment, with natural language descriptions.
Norm-Aware Embedding for Efficient Person Search
Person Search is a practically relevant task that aims to jointly solve Person Detection and Person Re-identification (re-ID).
Symbiotic Adversarial Learning for Attribute-based Person Search
The current state-of-the-art methods either focus on learning better cross-modal embeddings by mining only seen data, or they explicitly use generative adversarial networks (GANs) to synthesize unseen features.
Multi-Attribute Enhancement Network for Person Search
Visual character attributes play a key role in retrieving the query person, which has been explored in Re-ID but has been ignored in Person Search.
Sequential End-to-end Network for Efficient Person Search
Person search aims at jointly solving Person Detection and Person Re-identification (re-ID).
Anchor-Free Person Search
Person search aims to simultaneously localize and identify a query person from realistic, uncropped images, which can be regarded as the unified task of pedestrian detection and person re-identification (re-id).
Improved Instance Discrimination and Feature Compactness for End-to-End Person Search
Our method achieves comparable performance on two benchmarks, CUHK-SYSU and PRW, and achieves 91. 96% of mAP and 93. 34% of rank1 accuracy on CUHK-SYSU.