Paper Reading AI Learner

Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification

2019-04-10 02:04:24
Lingxiao He, Yinggang Wang, Wu Liu, Xingyu Liao, He Zhao, Zhenan Sun, Jiashi Feng

Abstract

Re-identifying a person across multiple disjoint camera views is important for intelligent video surveillance, smart retailing and many other applications. However, existing person re-identification (ReID) methods are challenged by the ubiquitous occlusion over persons and suffer from performance degradation. This paper proposes a novel occlusion-robust and alignment-free model for occluded person ReID and extends its application to realistic and crowded scenarios. The proposed model first leverages the full convolution network (FCN) and pyramid pooling to extract spatial pyramid features. Then an alignment-free matching approach, namely Foreground-aware Pyramid Reconstruction (FPR), is developed to accurately compute matching scores between occluded persons, despite their different scales and sizes. FPR uses the error from robust reconstruction over spatial pyramid features to measure similarities between two persons. More importantly, we design an occlusion-sensitive foreground probability generator that focuses more on clean human body parts to refine the similarity computation with less contamination from occlusion. The FPR is easily embedded into any end-to-end person ReID models. The effectiveness of the proposed method is clearly demonstrated by the experimental results (Rank-1 accuracy) on three occluded person datasets: Partial REID (78.30\%), Partial iLIDS (68.08\%) and Occluded REID (81.00\%); and three benchmark person datasets: Market1501 (95.42\%), DukeMTMC (88.64\%) and CUHK03 (76.08\%)

Abstract (translated)

在多个不相交的摄像头视图中重新识别一个人对于智能视频监控、智能零售和许多其他应用程序都很重要。然而,现有的人再识别(REID)方法面临着普遍存在的人身闭塞的挑战,并且性能下降。本文提出了一种新的被遮挡人REID的遮挡鲁棒无对齐模型,并将其应用扩展到现实和拥挤的场景中。该模型首先利用全卷积网络和金字塔池提取空间金字塔特征。然后开发了一种无对齐匹配方法,即前景感知金字塔重建(FPR),以精确计算被遮挡者之间的匹配分数,尽管他们的规模和大小不同。FPR利用空间金字塔特征上的鲁棒重构误差来测量两个人之间的相似性。更重要的是,我们设计了一个对遮挡敏感的前景概率生成器,它将更多的注意力集中在清洁的人体部位,以减少遮挡造成的污染,从而优化相似性计算。FPR很容易嵌入到任何端到端的个人REID模型中。实验结果表明,该方法的有效性(秩1精度)在三个封闭人数据集上得到了明确的证明:部分REID(78.30%)、部分ILID(68.08%)和封闭REID(81.00%);三个基准人数据集:Market1501(95.42%)、Dukemtmc(88.64%)和Cuhk03(76.08%)。

URL

https://arxiv.org/abs/1904.04975

PDF

https://arxiv.org/pdf/1904.04975.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