Paper Reading AI Learner

Deep Learning Based Person Re-Identification Methods: A Survey and Outlook of Recent Works

2021-10-10 11:23:47
Zhangqiang Ming, Min Zhu, Xiaoyong Wei, Xiangkun Wang, Jiamin Zhu, Junlong Cheng, Yong Yang

Abstract

In recent years, with the increasing demand for public safety and the rapid development of intelligent surveillance networks, person re-identification (Re-ID) has become one of the hot research topics in the field of computer vision. Its main research goal is to retrieve persons with the same identity from different cameras. However, traditional person Re-ID methods require manual marking of person targets, which consumes a lot of labor costs. With the widespread application of deep neural networks in the field of computer vision, a large number of deep learning-based person Re-ID methods have emerged. To facilitate researchers to better understand the latest research results and future development trends in this field. Firstly, we compare traditional and deep learning-based person Re-ID methods, and present the main contributions of several person Re-ID surveys, and analyze their focused dimensions and shortcomings. Secondly, we focus on the current classic deep learning-based person Re-ID methods, including methods for deep metric learning, local feature learning, generate adversarial networks, sequence feature learning, and graph convolutional networks. Furthermore, we subdivide the above five categories according to their technique types, analyzing and comparing the experimental performance of part subcategories of the method. Finally, we discuss the challenges that remain in the field of person Re-ID field and prospects for future research directions.

Abstract (translated)

URL

https://arxiv.org/abs/2110.04764

PDF

https://arxiv.org/pdf/2110.04764.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot