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Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving.
Deep learning strong parts for pedestrian detection
Yonglong Tian, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 1912
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Histograms of oriented gradients for human detection
Navneet Dalal and Bill Triggs · 2005
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Human activity recognition in thermal infrared imagery
Ju Han and Bir Bhanu · 2005
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Fusion of color and infrared video for moving human detection
Ju Han and Bir Bhanu · 2007
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Pedestrian detection: A benchmark
Piotr Dollár, Christian Wojek, Bernt Schiele, and Pietro Perona · 2009
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An HOG-LBP human detector with partial occlusion handling
Xiaoyu Wang, Tony X Han, and Shuicheng Yan · 2009
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Object detection with discriminatively trained part-based models
Pedro F Felzenszwalb, Ross B Girshick, David McAllester, and Deva Ramanan · 2010
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Multimodal deep learning
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y Ng · 2011
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Adapting pedestrian detection from synthetic to far infrared images
Yainuvis Socarrás, Sebastian Ramos, David Vázquez, Antonio M López, and Theo Gevers · 2011
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Pedestrian detection at 100 frames per second
Rodrigo Benenson, Markus Mathias, Radu Timofte, and Luc Van Gool · 2012
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Pedestrian detection: An evaluation of the state of the art
Piotr Dollar, Christian Wojek, Bernt Schiele, and Pietro Perona · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Convolutional-recursive deep learning for 3d object classification
Richard Socher, Brody Huval, Bharath Bath, Christopher D Manning, and Andrew Y Ng · 2012
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Multimodal learning with deep boltzmann machines
Nitish Srivastava and Ruslan R Salakhutdinov · 2012
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An iterative integrated framework for thermal–visible image registration, sensor fusion, and people tracking for video surveillance applications
Atousa Torabi, Guillaume Massé, and Guillaume-Alexandre Bilodeau · 2012
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Min Lin, Qiang Chen, and Shuicheng Yan · 2013
Cited alongside, same era.
Random forests of local experts for pedestrian detection
Javier Marin, David Vázquez, Antonio M López, Jaume Amores, and Bastian Leibe · 2013
Cited alongside, same era.
Handling occlusions with franken-classifiers
Markus Mathias, Rodrigo Benenson, Radu Timofte, and Luc Gool · 2013
Cited alongside, same era.
Modeling mutual visibility relationship in pedestrian detection
Wanli Ouyang, Xingyu Zeng, and Xiaogang Wang · 2013
Cited alongside, same era.
Pedestrian detection with unsupervised multi-stage feature learning
Pierre Sermanet, Koray Kavukcuoglu, Soumith Chintala, and Yann LeCun · 2013
Cited alongside, same era.
Ten years of pedestrian detection, what have we learned?
Rodrigo Benenson, Mohamed Omran, Jan Hosang, and Bernt Schiele · 2014
Fast R-CNN
Ross Girshick · 2015
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Taking a deeper look at pedestrians
Jan Hosang, Mohamed Omran, Rodrigo Benenson, and Bernt Schiele · 2015
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Multispectral pedestrian detection: Benchmark dataset and baseline
Soonmin Hwang, Jaesik Park, Namil Kim, Yukyung Choi, and In So Kweon · 2015
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Scale-aware fast r-cnn for pedestrian detection
Jianan Li, Xiaodan Liang, ShengMei Shen, Tingfa Xu, and Shuicheng Yan · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Deep perceptual mapping for thermal to visible face recognition
M Saquib Sarfraz and Rainer Stiefelhagen · 2015
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Cited alongside, same era.
Fast feature pyramids for object detection
Piotr Dollár, Ron Appel, Serge Belongie, and Pietro Perona · 2014
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
Cited alongside, same era.
Learning rich features from rgb-d images for object detection and segmentation
Saurabh Gupta, Ross Girshick, Pablo Arbeláez, and Jitendra Malik · 2014
Cited alongside, same era.
Large-scale video classification with convolutional neural networks
Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei · 2014
Cited alongside, same era.
Local decorrelation for improved pedestrian detection
Woonhyun Nam, Piotr Dollár, and Joon Hee Han · 2014
Cited alongside, same era.
Strengthening the effectiveness of pedestrian detection with spatially pooled features
Sakrapee Paisitkriangkrai, Chunhua Shen, and Anton van den Hengel · 2014
Cited alongside, same era.
Later among the works it cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Learning deep structure-preserving image-text embeddings
Liwei Wang, Yin Li, and Svetlana Lazebnik · 2015
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Deep correlation for matching images and text
Fei Yan and Krystian Mikolajczyk · 2015
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Filtered channel features for pedestrian detection
Shanshan Zhang, Rodrigo Benenson, and Bernt Schiele · 2015
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Pedestrian detection at day/night time with visible and fir cameras: A comparison
Alejandro González, Zhijie Fang, Yainuvis Socarras, Joan Serrat, David Vázquez, Jiaolong Xu, and Antonio M López · 2016
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Deep learning
Yoshua Bengio Ian Goodfellow and Aaron Courville · 2016
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People detection in crowded scenes by context-driven label propagation
Jingjing Liu, Quanfu Fan, Sharath Pankanti, and Dimitris N Metaxas · 2016
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Multispectral pedestrian detection using deep fusion convolutional neural networks
Jörg Wagner, Volker Fischer, Michael Herman, and Sven Behnke · 2016
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How far are we from solving pedestrian detection?
Shanshan Zhang, Rodrigo Benenson, Mohamed Omran, Jan Hosang, and Bernt Schiele · 2016
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