整理的行人搜索（Person Search）的一些文献和相关资料 | Awesome Person Search A naive list of Person Search related material.😅
Overview
行人搜索的工作主要是将行人检测（detection）以及行人重识别（Re-ID）整合在一起的工作。相比被广泛研究的单纯的行人重识别要更加贴近于现实的应用。
Paper List of Person Search
| Paper | Source | Related Material | Rating | CUHK(mAP/Top-1) | PRW(mAP/Top-1) |
|---|---|---|---|---|---|
| A Discriminatively Learned Feature Embedding Based on Multi-Loss Fusion For Person Search | ICASSP 2018 | - | ✩ | 79.78/79.90 | 21.00/63.10 |
| Correlation Based Identity Filter: An Efficient Framework for Person Search | ICIG 2017 | - | ✩✩ | 43.35/50.48 | - |
| Joint Detection and Identification Feature Learning for Person Search | CVPR2 017 | Code | ✩✩✩ | 75.5/77.9 | - |
| Enhanced Deep Feature Representation for Person Search | CCCV 2017 | - | ✩ | 77.8/80.6 | - |
| IAN: The Individual Aggregation Network for Person Search | PR 2019 | - | ✩ | 77.23/80.45 | - |
| Instance Enhancing Loss: Deep Identity-Sensitive Feature Embedding For Person Search | ICIP 2018 | - | ✩✩ | 79.43/79.66 | 24.26/69.47 |
| Neural Person Search Machines | ICCV 2017 | - | ✩✩ | 77.9/81.2 | 24.2/53.1 |
| Person Re-identification in the Wild | CVPR 2017 | Baseline | ✩✩ | - | 20.5/48.3 |
| Person Search by Multi-Scale Matching | ECCV 2018 | - | ✩✩ | 87.2/88.5 | 38.7/65.0 |
| Person Search via A Mask-Guided Two-Stream CNN Model | ECCV 2018 | - | ✩✩ | 83.0/83.7 | 32.6/72.1 |
| Person Search in a Scene by Jointly Modeling People Commonness and Person Uniqueness | ACM MM 2014 | - | ✩✩ | - | - |
| RCAA: Relational context-aware agents for person search | ECCV 2018 | - | ✩✩ | 79.3/81.3 | |
| End-to-End Detection and Re-identification Integrated Net for Person Search | ACCV 2018 | - | ✩ | 79.5/81.5 | 25.6/48.7 |
| Learning Context Graph for Person Search | CVPR 2019 | Code | ✩✩✩ | 84.1/86.5 | 33.4/73.6 |
| Query-guided End-to-End Person Search | CVPR 2019 | Home | ✩✩✩ | 88.9/89.1 | 39.1/80.0 |
| Partially Separated Networks for Person Search | PCM 2018 | - | ✩✩ | 86.8/89.1 | 30.0/53.3 |
| A cascaded multitask network with deformable spatial transform on person search | IJARSys 2019 | - | ✩✩ | 84.1/84.5 | 34.3/68.4 |
| Multilevel Collaborative Attention Network for Person Search | ACCV 2018 | - | ✩✩ | 76.3/79.2 | 29.1/60.1 |
| Enhancing Person Retrieval with Joint Person Detection, Attribute Learning, and Identification | PCM 2018 | - | ✩ | - | 24.8/65.5 |
| Spatial Invariant Person Search Network | PRCV 2018 | Code | ✩✩ | 85.3/86.0 | 39.5/59.2 |
| FMT: fusing multi-task convolutional neural network for person search | MTAP 2019 | - | ✩ | 77.15/79.83 | - |
| Segmentation Mask Guided End-to-End Person Search | Image Communication 2020 | Dataset | ✩✩ | 86.3/86.5 | 26.7/64.0 |
| DHFF: Robust Multi-Scale Person Search by Dynamic Hierarchical Feature Fusion | ICIP 2019 | - | ✩✩ | 90.2/91.7 | 41.1/70.1 |
| Comprehensive Samples Constrain for Person Search | VCIP 2018 | - | ✩ | 81.5/81.8 | - |
| Fast Person Search Pipeline | ICME 2019 | - | ✩✩ | 86.99/89.87 | 44.45/70.58 |
| End-To-End Person Search Sequentially Trained On Aggregated Dataset | ICIP 2019 | - | ✩✩ | 79.4/80.5 | 29.4/31.9 |
| Scale Voting With Pyramidal Feature Fusion Network for Person Search | IEEE Access 2019 | - | ✩✩ | 84.5/89.8 | 34.3/73.9 |
| Structure-aware person search with self-attention and online instance aggregation matching | Neurocomputing | Code | ✩ | 76.98/77.86 | - |
| Knowledge Distillation for End-to-End Person Search | arXiv | - | ✩✩ | 85.0/85.5 | - |
| Re-ID Driven Localization Refinement for Person Search | ICCV 2019 | - | ✩✩✩ | 93.0/94.2 | 42.9/70.2 |
| Person Search Based on Improved Joint Learning Network | CASE 2019 | - | ✩ | 84.1/84.6 | 37.6/71.1 |
| Dynamic imposter based online instance matching for person search | PR 2019 | - | ✩✩ | 83.8/84.6 | 30.4/71.5 |
| Person Search with Joint Detection, Segmentation and Re-identification | HCC 2019 | - | ✩✩ | - | 24.35/53.73 |
| Improving Person Search by Adaptive Feature Pyramid-based Multi-Scale Matching | VCIP 2019 | - | ✩✩ | 81.2/81.5 | - |
| Person Search Based on Attention Mechanism | ISCIT 2019 | - | ✩ | 78.9/81.9 | - |
| Hierarchical Online Instance Matching for Person Search | AAAI 2020 | Code | ✩✩✩ | 89.7/90.8 | 39.8/80.4 |
| Person Search by Separated Modeling and A Mask-Guided Two-Stream CNN Model | TIP 2020 | - | ✩✩ | 83.3/83.9 | 32.8/72.1 |
| Person Search via Deep Integrated Networks | ApplSci 2020 | - | - | - | - |
| Improved Model Structure with Cosine Margin OIM Loss for End-to-End Person Search | MMM 2020 | - | ✩✩ | 83.5/84.8 | 32.8/72.2 |
| Norm-Aware Embedding for Efficient Person Search | CVPR 2020 | - | ✩✩✩ | 92.1/92.9 | 44.0/81.1 |
| Efficient Person Search via Expert-Guided Knowledge Distillation | TCYB 2019 | - | ✩✩ | 91.1/91.9 | 34.5/59.9 |
| An Iterative unsupervised Person Search Algorithm on Natural Scene Images | CAC 2019 | - | ✩✩ | 41.14/40.93 | 21.74/35.97 |
| GAN-based person search via deep complementary classifier with center-constrained Triplet loss | PR 2020 | - | ✩ | 77.89/78.34 | 53.09/70.39 |
| Robust Partial Matching for Person Search in the Wild | CVPR 2020 | - | ✩✩✩ | 88.9/89.3 | 41.9/81.4 |
| End-to-End Thorough Body Perception for Person Search | AAAI 2020 | - | ✩✩ | 88.4/90.5 | 48.5/87.9 |
| Instance Guided Proposal Network for Person Search | CVPR 2020 | - | ✩✩✩ | 90.3/91.4 | 47.2/87.0 |
| TCTS: A Task-Consistent Two-stage Framework for Person Search | CVPR 2020 | - | ✩✩✩ | 93.9/95.1 | 46.8/87.5 |
| Bi-directional Interaction Network for Person Search | CVPR 2020 | - | ✩✩✩ | 90.0/90.7 | 45.3/81.7 |
| Joint Person Objectness and Repulsion for PersonSearch | TIP 2020 | - | ✩✩ | 93.23/93.83 | 52.30/71.51 |
| Deep Learning-based Person Search with Visual Attention Embedding | COMM 2020 | - | ✩ | 78.3/79.2 | 35.8/84.7 |
| A Modified Pedestrian Retrieval Method Based on Faster R-CNN with Integration of Pedestrian Detection and Re-Identification | ICALIP 2018 | ||||
| Bottom-Up Foreground-Aware Feature Fusion for Person Search | ACM MM 2020 | - | ✩✩✩ | 90.7/91.6 | 42.2/81.0 |
| Dual Context-Aware Refinement Network for Person Search | ACM MM 2020 | - | ✩✩ | 87.5/88.7 | 38.8/77.7 |
| Tasks Integrated Networks: Joint Detection andRetrieval for Image Search | TPAMI 2019 | - | ✩✩ | 86.2/86.5 | 31.8/55.1 |
| Person Search via Anchor-Free Detection and Part-Based Group Feature Similarity Estimation | PRCV 2020 | - | ✩ | 78.2/88.7 | 57.8/72.3 |
| Identity-sensitive loss guided and instance feature boosted deepembedding for person search | Neurocomputing 2020 | - | ✩ | 81.36/82.38 | 26.52/74.72 |
| Diverse Knowledge Distillation for End-to-End Person Search | AAAI 2021 | Code | ✩✩✩ | 93.09/94.24 | 50.51/87.07 |
| Learning Discriminative Part Features Through Attentions For Effective And Scalable Person Search | ICIP 2020 | - | ✩ | 85.7/87.6 | 43.5/84.0 |
| Weakly Supervised Person Search | DSAA 2020 | ||||
| Anchor-Free Detector and Re-Identification with Joint Loss for PersonSearch | MCTE 2020 | - | ✩✩ | 89.4/91.7 | 34.3/74.1 |
Description of the rating$^{*}$:
- ✩ = Lack of Innovation
- ✩✩ = Inspiring
- ✩✩✩ = Insightful
* According to personal opinion
Related Paper List
| Paper | Source | Related Material |
|---|---|---|
| Fusion-Attention Network for person search with free-form natural language | PRL 2018 | - |
| Cascade Attention Network for Person Search: Both Image and Text-Image Similarity Selection | arXiv | - |
| Deep Adversarial Graph Attention Convolution Network for Text-Based Person Search | ACM MM | - |
| Language Person Search with Mutually Connected Classification Loss | ICASSP 2019 | - |
| Improving Text-Based Person Search by Spatial Matching and Adaptive Threshold | WACV 2018 | - |
| Person Search in Videos with One Portrait Through Visual and Temporal Links | ECCV 2018 | Home Code |
| Person Search with Natural Language Description | CVPR 2017 | Code |
| Cross-Modal Cross-Domain Moment Alignment Network for Person Search | CVPR 2020 | - |
Dataset
基于这两个数据集，有工作对部分行人图像进行了mask标注
Docker Image for JDI-PS
- 首先拉取Docker镜像：https://hub.docker.com/r/4f5da2/person_search
- 在准备数据集和代码， 并对代码进行一下修改
- 在
tools/demo.py中的import matplotlib下添加matplotlib.use('Agg')来避免对GUI相关功能的调用（因为是在docker里） - 由于protobuf版本发生变化，需在
lib/fast_rcnn/train.py中增加一行import google.protobuf.text_format - 按照Github上对应的readme进行编译，填入docker镜像中cuDNN的路径如下：
cmake .. -DUSE_MPI=ON -DCUDNN_INCLUDE=/usr/include -DCUDNN_LIBRARY=/usr/lib/x86_64-linux-gnu/libcudnn.so
- 在
- 编译Caffe框架并进行训练