暂无分享,去创建一个
[1] B. Schiele,et al. How Far are We from Solving Pedestrian Detection? , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Bernt Schiele,et al. What Makes for Effective Detection Proposals? , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[3] Yuning Jiang,et al. What Can Help Pedestrian Detection? , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Changick Kim,et al. Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Jun Li,et al. Filtered shallow-deep feature channels for pedestrian detection , 2017, Neurocomputing.
[6] Luc Van Gool,et al. Handling Occlusions with Franken-Classifiers , 2013, 2013 IEEE International Conference on Computer Vision.
[7] Yuxin Peng,et al. Discriminative latent semantic feature learning for pedestrian detection , 2017, Neurocomputing.
[8] Weinan Zhang,et al. Pedestrian Detection by Feature Selected Self-Similarity Features , 2018, IEEE Access.
[9] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Xuelong Li,et al. Learning Multilayer Channel Features for Pedestrian Detection , 2016, IEEE Transactions on Image Processing.
[11] Xiaogang Wang,et al. Multi-source Deep Learning for Human Pose Estimation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[12] Shuicheng Yan,et al. Scale-Aware Fast R-CNN for Pedestrian Detection , 2015, IEEE Transactions on Multimedia.
[13] Michael Ying Yang,et al. Real-time RGB-D based template matching pedestrian detection , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[14] Dariu Gavrila,et al. EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[15] Hau-San Wong,et al. Exploiting Target Data to Learn Deep Convolutional Networks for Scene-Adapted Human Detection , 2018, IEEE Transactions on Image Processing.
[16] Chih-Yang Lin,et al. PedJointNet: Joint Head-Shoulder and Full Body Deep Network for Pedestrian Detection , 2019, IEEE Access.
[17] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[18] Xiaoming Liu,et al. Illuminating Pedestrians via Simultaneous Detection and Segmentation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[19] Jiahuan Zhou,et al. Fused Discriminative Metric Learning for Low Resolution Pedestrian Detection , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).
[20] Rogério Schmidt Feris,et al. A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection , 2016, ECCV.
[21] Yi Zhou,et al. Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance , 2020, IEEE Transactions on Intelligent Transportation Systems.
[22] Hui Zhang,et al. Joint Holistic and Partial CNN for Pedestrian Detection , 2018, BMVC.
[23] Jian Yang,et al. DSFD: Dual Shot Face Detector , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Haibin Ling,et al. Differential Features for Pedestrian Detection: A Taylor Series Perspective , 2019, IEEE Transactions on Intelligent Transportation Systems.
[25] Huimin Ma,et al. Semantic Head Enhanced Pedestrian Detection in a Crowd , 2019, Neurocomputing.
[26] Silvio Savarese,et al. Subcategory-Aware Convolutional Neural Networks for Object Proposals and Detection , 2016, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).
[27] Shugong Xu,et al. Human Detection Aided by Deeply Learned Semantic Masks , 2020, IEEE Transactions on Circuits and Systems for Video Technology.
[28] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[29] Yang Zhao,et al. Deep High-Resolution Representation Learning for Visual Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] Badong Chen,et al. Accurate Pedestrian Detection by Human Pose Regression , 2020, IEEE Transactions on Image Processing.
[31] Ling Shao,et al. Pedestrian Detection: The Elephant In The Room , 2020, ArXiv.
[32] Yizhou Wang,et al. Multi-Level Domain Adaptive Learning for Cross-Domain Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[33] Nuno Vasconcelos,et al. Learning Complexity-Aware Cascades for Deep Pedestrian Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[34] Namil Kim,et al. Multispectral pedestrian detection: Benchmark dataset and baseline , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Shifeng Zhang,et al. Single-Shot Refinement Neural Network for Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[36] Yu-Wing Tai,et al. Accurate Single Stage Detector Using Recurrent Rolling Convolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Junjie Yan,et al. Mimicking Very Efficient Network for Object Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Badong Chen,et al. Training Cascade Compact CNN With Region-IoU for Accurate Pedestrian Detection , 2020, IEEE Transactions on Intelligent Transportation Systems.
[39] Yann LeCun,et al. Pedestrian Detection with Unsupervised Multi-stage Feature Learning , 2012, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[40] Qixiang Ye,et al. CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection , 2020, IEEE Transactions on Intelligent Transportation Systems.
[41] Cordelia Schmid,et al. LCR-Net++: Multi-Person 2D and 3D Pose Detection in Natural Images , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[42] Cong Phuoc Huynh,et al. Domain-Adaptive Pedestrian Detection in Thermal Images , 2019, 2019 IEEE International Conference on Image Processing (ICIP).
[43] David A. McAllester,et al. Cascade object detection with deformable part models , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[44] Shifeng Zhang,et al. PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes , 2019, AAAI.
[45] Michael Ying Yang,et al. Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection , 2018, Inf. Fusion.
[46] Luc Van Gool,et al. Depth and Appearance for Mobile Scene Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[47] Luc Van Gool,et al. Domain Adaptive Faster R-CNN for Object Detection in the Wild , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[48] Xu Zhao,et al. Led: Localization-Quality Estimation Embedded Detector , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).
[49] Krystian Mikolajczyk,et al. Domain Adaptation for Object Detection via Style Consistency , 2019, BMVC.
[50] Ming Yang,et al. Discriminative Feature Transformation for Occluded Pedestrian Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[51] Bernard Ghanem,et al. Semantic Part RCNN for Real-World Pedestrian Detection , 2019, CVPR Workshops.
[52] Bingbing Ni,et al. Learning Context Graph for Person Search , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[53] Alfred O. Hero,et al. Part-Level Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box Alignment , 2018, IEEE Access.
[54] Ling Shao,et al. Towards Bridging Semantic Gap to Improve Semantic Segmentation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[55] Chunhua Shen,et al. Pushing the Limits of Deep CNNs for Pedestrian Detection , 2016, IEEE Transactions on Circuits and Systems for Video Technology.
[56] Xuelong Li,et al. Taking a Look at Small-Scale Pedestrians and Occluded Pedestrians , 2019, IEEE Transactions on Image Processing.
[57] Margrit Betke,et al. Learning to Separate: Detecting Heavily-Occluded Objects in Urban Scenes , 2020, ECCV.
[58] Ujjwal,et al. A One-and-Half Stage Pedestrian Detector , 2020, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).
[59] Xuelong Li,et al. Speed up deep neural network based pedestrian detection by sharing features across multi-scale models , 2016, Neurocomputing.
[60] Liang Lin,et al. Is Faster R-CNN Doing Well for Pedestrian Detection? , 2016, ECCV.
[61] Ming Yang,et al. Self-Mimic Learning for Small-scale Pedestrian Detection , 2020, ACM Multimedia.
[62] Jan P. Allebach,et al. Pedestrian Detection Using Pixel Difference Matrix Projection , 2020, IEEE Transactions on Intelligent Transportation Systems.
[63] Jiwen Lu,et al. Multi-Grained Deep Feature Learning for Robust Pedestrian Detection , 2019, IEEE Transactions on Circuits and Systems for Video Technology.
[64] Koen E. A. van de Sande,et al. Selective Search for Object Recognition , 2013, International Journal of Computer Vision.
[65] Chengyang Li,et al. Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation , 2018, BMVC.
[66] Matti Pietikäinen,et al. Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[67] Xiaogang Wang,et al. Jointly Learning Deep Features, Deformable Parts, Occlusion and Classification for Pedestrian Detection , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[68] Xiaogang Wang,et al. Joint Deep Learning for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.
[69] Pietro Perona,et al. The Fastest Pedestrian Detector in the West , 2010, BMVC.
[70] C.-C. Jay Kuo,et al. Boosted Convolutional Neural Networks (BCNN) for Pedestrian Detection , 2017, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).
[71] David A. McAllester,et al. Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[72] Gregory Shakhnarovich,et al. Task-Driven Super Resolution: Object Detection in Low-resolution Images , 2018, ICONIP.
[73] Ujjwal Bhattacharya,et al. ClueNet : A Deep Framework for Occluded Pedestrian Pose Estimation , 2019, BMVC.
[74] Shiliang Pu,et al. Small-Scale Pedestrian Detection Based on Topological Line Localization and Temporal Feature Aggregation , 2018, ECCV.
[75] Lu Wang,et al. Pedestrian detection in crowded scenes via scale and occlusion analysis , 2016, 2016 IEEE International Conference on Image Processing (ICIP).
[76] Kai Chen,et al. Region Proposal by Guided Anchoring , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[77] Yichen Wei,et al. Relation Networks for Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[78] Meng Wang,et al. Scene-Specific Pedestrian Detection for Static Video Surveillance , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[79] Anelia Angelova,et al. Real-Time Pedestrian Detection with Deep Network Cascades , 2015, BMVC.
[80] Yi Lu Murphey,et al. Spatial Focal Loss for Pedestrian Detection in Fisheye Imagery , 2019, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).
[81] Nuno Vasconcelos,et al. Cascade R-CNN: High Quality Object Detection and Instance Segmentation , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[82] Hui Zhou,et al. Pedestrian Detection via Body Part Semantic and Contextual Information With DNN , 2018, IEEE Transactions on Multimedia.
[83] Changxin Gao,et al. Re-ID Driven Localization Refinement for Person Search , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[84] Maneesh Singh,et al. Progressive Domain Adaptation for Object Detection , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).
[85] Shuo Yang,et al. WIDER FACE: A Face Detection Benchmark , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[86] G LoweDavid,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004 .
[87] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[88] Xuelong Li,et al. Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry , 2015, IEEE Transactions on Image Processing.
[89] Wenyu Liu,et al. Neural features for pedestrian detection , 2017, Neurocomputing.
[90] Qixiang Ye,et al. Scale Match for Tiny Person Detection , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).
[91] Ziyan Wu,et al. A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[92] Xuelong Li,et al. Cascaded Subpatch Networks for Effective CNNs , 2016, IEEE Transactions on Neural Networks and Learning Systems.
[93] Luc Van Gool,et al. Seeking the Strongest Rigid Detector , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[94] Nicu Sebe,et al. Learning Cross-Modal Deep Representations for Robust Pedestrian Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[95] Jungwon Lee,et al. Fused DNN: A Deep Neural Network Fusion Approach to Fast and Robust Pedestrian Detection , 2016, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).
[96] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[97] Joohee Kim,et al. Object Detection With Location-Aware Deformable Convolution and Backward Attention Filtering , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[98] Xiaoming Liu,et al. Pedestrian Detection With Autoregressive Network Phases , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[99] Xiaogang Wang,et al. Switchable Deep Network for Pedestrian Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[100] Kihong Park,et al. Unified multi-spectral pedestrian detection based on probabilistic fusion networks , 2018, Pattern Recognit..
[101] Bin Yang,et al. Convolutional Channel Features , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[102] David Gerónimo Gómez,et al. Survey of Pedestrian Detection for Advanced Driver Assistance Systems , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[103] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[104] Xiaogang Wang,et al. Deep Learning Strong Parts for Pedestrian Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[105] Rita Cucchiara,et al. Multistage Particle Windows for Fast and Accurate Object Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[106] Shuicheng Yan,et al. An HOG-LBP human detector with partial occlusion handling , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[107] Xuelong Li,et al. Learning Sampling Distributions for Efficient Object Detection , 2015, IEEE Transactions on Cybernetics.
[108] Shifeng Zhang,et al. WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild , 2019, IEEE Transactions on Multimedia.
[109] Guofa Li,et al. Deep Learning Approaches on Pedestrian Detection in Hazy Weather , 2020, IEEE Transactions on Industrial Electronics.
[110] Sebastian Ramos,et al. The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[111] Bernt Schiele,et al. Ten Years of Pedestrian Detection, What Have We Learned? , 2014, ECCV Workshops.
[112] Gustavo Carneiro,et al. Improving the performance of pedestrian detectors using convolutional learning , 2017, Pattern Recognit..
[113] Xiangyu Zhu,et al. Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[114] Christopher Hunt,et al. Notes on the OpenSURF Library , 2009 .
[115] Alberto Del Bimbo,et al. Task-Conditioned Domain Adaptation for Pedestrian Detection in Thermal Imagery , 2020, ECCV.
[116] Kate Saenko,et al. Strong-Weak Distribution Alignment for Adaptive Object Detection , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[117] Jian Wang,et al. JCS-Net: Joint Classification and Super-Resolution Network for Small-Scale Pedestrian Detection in Surveillance Images , 2019, IEEE Transactions on Information Forensics and Security.
[118] Shifeng Zhang,et al. Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd , 2018, ECCV.
[119] Shengcai Liao,et al. Robust Multi-resolution Pedestrian Detection in Traffic Scenes , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[120] Nuno Vasconcelos,et al. Cascade R-CNN: Delving Into High Quality Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[121] Huimin Ma,et al. Occluded Pedestrian Detection with Visible IoU and Box Sign Predictor , 2019, 2019 IEEE International Conference on Image Processing (ICIP).
[122] Fahad Shahbaz Khan,et al. Count- and Similarity-Aware R-CNN for Pedestrian Detection , 2020, ECCV.
[123] Hong Qiao,et al. Cross-modality interactive attention network for multispectral pedestrian detection , 2019, Inf. Fusion.
[124] Tania Stathaki,et al. Coupled Network for Robust Pedestrian Detection With Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling , 2019, IEEE Transactions on Image Processing.
[125] Yan Yan,et al. Local Co-Occurrence Selection via Partial Least Squares for Pedestrian Detection , 2017, IEEE Transactions on Intelligent Transportation Systems.
[126] Bernt Schiele,et al. Filtered channel features for pedestrian detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[127] Paul A. Viola,et al. Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.
[128] Michelle Karg,et al. NightOwls: A Pedestrians at Night Dataset , 2018, ACCV.
[129] Xun Cao,et al. Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems , 2020, ECCV.
[130] Michael Ying Yang,et al. Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.
[131] Xiaogang Wang,et al. Joint Detection and Identification Feature Learning for Person Search , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[132] Rama Chellappa,et al. HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[133] Arthur Daniel Costea,et al. Fast Boosting Based Detection Using Scale Invariant Multimodal Multiresolution Filtered Features , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[134] Xiaogang Wang,et al. Single-Pedestrian Detection Aided by Two-Pedestrian Detection , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[135] Anton van den Hengel,et al. Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[136] Fahad Shahbaz Khan,et al. PSC-Net: Learning Part Spatial Co-occurence for Occluded Pedestrian Detection , 2020, ArXiv.
[137] Pong C. Yuen,et al. Robust Anchor Embedding for Unsupervised Video Person re-IDentification in the Wild , 2018, ECCV.
[138] Yali Li,et al. Fast Pedestrian Detection With Attention-Enhanced Multi-Scale RPN and Soft-Cascaded Decision Trees , 2020, IEEE Transactions on Intelligent Transportation Systems.
[139] Gang Wang,et al. Graininess-Aware Deep Feature Learning for Pedestrian Detection , 2018, ECCV.
[140] Wei Liu,et al. High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[141] Yoshua Bengio,et al. Generative Adversarial Networks , 2014, ArXiv.
[142] Armin B. Cremers,et al. Exploring Human Vision Driven Features for Pedestrian Detection , 2015, IEEE Transactions on Circuits and Systems for Video Technology.
[143] Larry S. Davis,et al. Soft-NMS — Improving Object Detection with One Line of Code , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[144] Jun Chu,et al. Object Detection Based on Multi-Layer Convolution Feature Fusion and Online Hard Example Mining , 2018, IEEE Access.
[145] Arthur Daniel Costea,et al. Semantic Channels for Fast Pedestrian Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[146] Bernt Schiele,et al. CityPersons: A Diverse Dataset for Pedestrian Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[147] Long Chen,et al. ZoomNet: Deep Aggregation Learning for High-Performance Small Pedestrian Detection , 2018, ACML.
[148] Steven L. Waslander,et al. Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[149] Fei-Yue Wang,et al. MFR-CNN: Incorporating Multi-Scale Features and Global Information for Traffic Object Detection , 2018, IEEE Transactions on Vehicular Technology.
[150] Xiaogang Wang,et al. Pedestrian detection aided by deep learning semantic tasks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[151] Xuelong Li,et al. Triply Supervised Decoder Networks for Joint Detection and Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[152] Li Cheng,et al. Too Far to See? Not Really!—Pedestrian Detection With Scale-Aware Localization Policy , 2017, IEEE Transactions on Image Processing.
[153] Zhe Chen,et al. A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection , 2019, ArXiv.
[154] Fahad Shahbaz Khan,et al. Color attributes for object detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[155] Bernt Schiele,et al. Taking a deeper look at pedestrians , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[156] Ming Yang,et al. Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward , 2020, Pattern Recognit..
[157] Joon Hee Han,et al. Local Decorrelation For Improved Pedestrian Detection , 2014, NIPS.
[158] Tao Chen,et al. S-CNN: Subcategory-Aware Convolutional Networks for Object Detection , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[159] Long Chen,et al. Learning Lightweight Pedestrian Detector with Hierarchical Knowledge Distillation , 2019, 2019 IEEE International Conference on Image Processing (ICIP).
[160] Yun Zhang,et al. Attention guided neural network models for occluded pedestrian detection , 2020, Pattern Recognit. Lett..
[161] Tania Stathaki,et al. SAM-RCNN: Scale-Aware Multi-Resolution Multi-Channel Pedestrian Detection , 2018, BMVC.
[162] Larry S. Davis,et al. FA-RPN: Floating Region Proposals for Face Detection , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[163] Shengcai Liao,et al. Person re-identification by Local Maximal Occurrence representation and metric learning , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[164] Lei Jin,et al. FPN++: A Simple Baseline for Pedestrian Detection , 2019, 2019 IEEE International Conference on Multimedia and Expo (ICME).
[165] Xinge Zhu,et al. Adapting Object Detectors via Selective Cross-Domain Alignment , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[166] R. Rajesh,et al. Pedestrian Detection in Automotive Safety: Understanding State-of-the-Art , 2019, IEEE Access.
[167] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[168] Xuelong Li,et al. High-Level Semantic Networks for Multi-Scale Object Detection , 2020, IEEE Transactions on Circuits and Systems for Video Technology.
[169] Yunhong Wang,et al. Adaptive NMS: Refining Pedestrian Detection in a Crowd , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[170] Pietro Perona,et al. Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[171] Gunhee Kim,et al. Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[172] Kaiming He,et al. Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[173] Arash Vahdat,et al. A Robust Learning Approach to Domain Adaptive Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[174] Zequn Jie,et al. NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[175] Bernt Schiele,et al. Towards Reaching Human Performance in Pedestrian Detection , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[176] Michael Ying Yang,et al. Unsupervised Domain Adaptation for Multispectral Pedestrian Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[177] Kaiming He,et al. Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[178] Xu Zhao,et al. Oriented Spatial Transformer Network for Pedestrian Detection Using Fish-Eye Camera , 2020, IEEE Transactions on Multimedia.
[179] Philip H. S. Torr,et al. BING: Binarized normed gradients for objectness estimation at 300fps , 2014, Computational Visual Media.
[180] Xinyu Zhang,et al. An Extended Filtered Channel Framework for Pedestrian Detection , 2018, IEEE Transactions on Intelligent Transportation Systems.
[181] Xiaogang Wang,et al. Single-Pedestrian Detection Aided by Multi-pedestrian Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[182] Nenghai Yu,et al. Learning Pixel-Level and Instance-Level Context-Aware Features for Pedestrian Detection in Crowds , 2019, IEEE Access.
[183] Jia Deng,et al. Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.
[184] Yaser Sheikh,et al. OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[185] Kaizhu Huang,et al. Hybrid Channel Based Pedestrian Detection , 2020, Neurocomputing.
[186] Pietro Perona,et al. Integral Channel Features , 2009, BMVC.
[187] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[188] Takeo Kanade,et al. Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance , 2018, International Journal of Computer Vision.
[189] Ming Tang,et al. PCN: Part and Context Information for Pedestrian Detection with CNNs , 2018, BMVC.
[190] Seong Joon Oh,et al. Person Recognition in Personal Photo Collections , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[191] Xiaogang Wang,et al. DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[192] Junhyuk Hyun,et al. A Pedestrian Detection System Accelerated by Kernelized Proposals , 2020, IEEE Transactions on Intelligent Transportation Systems.
[193] Bernt Schiele,et al. New features and insights for pedestrian detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[194] Lei Zhu,et al. Pedestrian Proposal and Refining Based on the Shared Pixel Differential Feature , 2019, IEEE Transactions on Intelligent Transportation Systems.
[195] Ruihao Yin,et al. Multi-Resolution Generative Adversarial Networks for Tiny-Scale Pedestrian Detection , 2019, 2019 IEEE International Conference on Image Processing (ICIP).
[196] Ming Yang,et al. Regionlets for Generic Object Detection , 2013, 2013 IEEE International Conference on Computer Vision.
[197] Chunluan Zhou,et al. Bi-box Regression for Pedestrian Detection and Occlusion Estimation , 2018, ECCV.
[198] Yuning Jiang,et al. Repulsion Loss: Detecting Pedestrians in a Crowd , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[199] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[200] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[201] Pietro Perona,et al. Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[202] Ki-Sang Hong,et al. Deep network aided by guiding network for pedestrian detection , 2017, Pattern Recognit. Lett..
[203] Fahad Shahbaz Khan,et al. Mask-Guided Attention Network for Occluded Pedestrian Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[204] Ming Yang,et al. Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[205] Yi Li,et al. R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.
[206] Shengcai Liao,et al. Generalizable Pedestrian Detection: The Elephant In The Room , 2020 .
[207] Kevin Zhang,et al. Double Anchor R-CNN for Human Detection in a Crowd , 2019, ArXiv.
[208] Minjuan Wang,et al. Deep Feature Fusion by Competitive Attention for Pedestrian Detection , 2019, IEEE Access.
[209] Hau-San Wong,et al. Semi-Supervised Human Detection via Region Proposal Networks Aided by Verification , 2020, IEEE Transactions on Image Processing.
[210] Yunchao Wei,et al. Perceptual Generative Adversarial Networks for Small Object Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[211] Jian Yang,et al. Occluded Pedestrian Detection Through Guided Attention in CNNs , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[212] Xuelong Li,et al. Hierarchical Shot Detector , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[213] Jie Wu,et al. PMC-GANs: Generating Multi-Scale High-Quality Pedestrian with Multimodal Cascaded GANs , 2019, BMVC.
[214] Jiashi Feng,et al. Few-Shot Adaptive Faster R-CNN , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[215] Wei Liu,et al. Learning Efficient Single-Stage Pedestrian Detectors by Asymptotic Localization Fitting , 2018, ECCV.
[216] Enhua Wu,et al. Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[217] Chunluan Zhou,et al. Multi-label Learning of Part Detectors for Heavily Occluded Pedestrian Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[218] Debasmita Ghose,et al. Pedestrian Detection in Thermal Images Using Saliency Maps , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[219] C. Lawrence Zitnick,et al. Edge Boxes: Locating Object Proposals from Edges , 2014, ECCV.
[220] Fan Yang,et al. Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[221] Xiangyu Zhang,et al. Detection in Crowded Scenes: One Proposal, Multiple Predictions , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[222] Dariu Gavrila,et al. Monocular Pedestrian Detection: Survey and Experiments , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[223] Mohan M. Trivedi,et al. Looking at Pedestrians at Different Scales: A Multiresolution Approach and Evaluations , 2016, IEEE Transactions on Intelligent Transportation Systems.
[224] Heiko Neumann,et al. Fully Convolutional Region Proposal Networks for Multispectral Person Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[225] Chengju Zhou,et al. Group Cost-Sensitive BoostLR With Vector Form Decorrelated Filters for Pedestrian Detection , 2020, IEEE Transactions on Intelligent Transportation Systems.
[226] Chengyang Li,et al. Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection , 2018, Pattern Recognit..
[227] Yanpeng Cao,et al. Pedestrian detection with unsupervised multispectral feature learning using deep neural networks , 2019, Inf. Fusion.
[228] Xiaogang Wang,et al. Unsupervised Salience Learning for Person Re-identification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[229] Jun Sun,et al. Where, What, Whether: Multi-modal Learning Meets Pedestrian Detection , 2020, CVPR 2020.
[230] Charless C. Fowlkes,et al. Multiresolution Models for Object Detection , 2010, ECCV.
[231] B. Schiele,et al. Multi-cue onboard pedestrian detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[232] Deva Ramanan,et al. Exploring Weak Stabilization for Motion Feature Extraction , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[233] Shifeng Zhang,et al. Relational Learning for Joint Head and Human Detection , 2019, AAAI.
[234] Shu Wang,et al. Multispectral Deep Neural Networks for Pedestrian Detection , 2016, BMVC.
[235] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[236] Ross B. Girshick,et al. Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[237] Ling Shao,et al. See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[238] Andreas Geiger,et al. Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[239] Luc Van Gool,et al. Pedestrian detection at 100 frames per second , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[240] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[241] Rémi Trichet,et al. LBP Channels for Pedestrian Detection , 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).
[242] Chao Liang,et al. S3D: Scalable Pedestrian Detection via Score Scale Surface Discrimination , 2020, IEEE Transactions on Circuits and Systems for Video Technology.
[243] Piotr Dollár,et al. Crosstalk Cascades for Frame-Rate Pedestrian Detection , 2012, ECCV.
[244] Yang Li,et al. Attribute-aware Pedestrian Detection in a Crowd , 2019 .
[245] Xinming Huang,et al. Pedestrian Detection for Autonomous Vehicle Using Multi-Spectral Cameras , 2019, IEEE Transactions on Intelligent Vehicles.
[246] Hanqing Lu,et al. Scale-Adaptive Deconvolutional Regression Network for Pedestrian Detection , 2016, ACCV.
[247] Fan Yang,et al. Single Shot Multibox Detector With Kalman Filter for Online Pedestrian Detection in Video , 2019, IEEE Access.
[248] Honggang Zhang,et al. Progressive Refinement Network for Occluded Pedestrian Detection , 2020, ECCV.
[249] Yi Yang,et al. Exploring Prior Knowledge for Pedestrian Detection , 2015, BMVC.
[250] Wei Liu,et al. SSD: Single Shot MultiBox Detector , 2015, ECCV.
[251] Tomaso A. Poggio,et al. A Trainable System for Object Detection , 2000, International Journal of Computer Vision.
[252] Mehul S. Raval,et al. ViS-HuD: Using Visual Saliency to Improve Human Detection with Convolutional Neural Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[253] Armin B. Cremers,et al. Informed Haar-Like Features Improve Pedestrian Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[254] Jiaolong Xu,et al. Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison , 2016, Sensors.
[255] Ali Farhadi,et al. You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[256] Anton van den Hengel,et al. Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features , 2014, ECCV.
[257] Haibin Ling,et al. A novel pixel neighborhood differential statistic feature for pedestrian and face detection , 2017, Pattern Recognit..
[258] Ki-Sang Hong,et al. Direct multi-scale dual-stream network for pedestrian detection , 2017, 2017 IEEE International Conference on Image Processing (ICIP).
[259] Xiangyu Zhang,et al. CrowdHuman: A Benchmark for Detecting Human in a Crowd , 2018, ArXiv.
[260] Andrew Y. Ng,et al. End-to-End People Detection in Crowded Scenes , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[261] David Hurych,et al. Advanced Pedestrian Dataset Augmentation for Autonomous Driving , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[262] Pong C. Yuen,et al. PurifyNet: A Robust Person Re-Identification Model With Noisy Labels , 2020, IEEE Transactions on Information Forensics and Security.