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Exploring RGB+Depth Fusion for Real-Time Object Detection
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-10584-0_23openalex-snapshot
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
inferredcites →https://doi.org/10.1007/978-3-319-10602-1_48openalex-snapshot
Microsoft COCO: Common Objects in Context
inferredcites →https://doi.org/10.1007/978-3-319-46448-0_2openalex-snapshot
SSD: Single Shot MultiBox Detector
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Real-Time Multispectral Pedestrian Detection with a Single-Pass Deep Neural Network
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inferredcites →https://doi.org/10.1109/avss.2018.8639110openalex-snapshot
Improving Real-Time Pedestrian Detectors with RGB+Depth Fusion
inferredcites →https://doi.org/10.1109/cvpr.2008.4587597openalex-snapshot
A discriminatively trained, multiscale, deformable part model
inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/cvpr.2012.6248017openalex-snapshot
Pedestrian detection at 100 frames per second
inferredcites →https://doi.org/10.1109/cvpr.2012.6248074openalex-snapshot
Are we ready for autonomous driving? The KITTI vision benchmark suite
inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2015.7298706openalex-snapshot
Multispectral pedestrian detection: Benchmark dataset and baseline
inferredcites →https://doi.org/10.1109/cvpr.2015.7298900openalex-snapshot
Probability occupancy maps for occluded depth images
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2016.91openalex-snapshot
You Only Look Once: Unified, Real-Time Object Detection
inferredcites →https://doi.org/10.1109/cvpr.2017.690openalex-snapshot
YOLO9000: Better, Faster, Stronger
inferredcites →https://doi.org/10.1109/cvprw.2017.36openalex-snapshot
Fully Convolutional Region Proposal Networks for Multispectral Person Detection
inferredcites →https://doi.org/10.1109/iccvw.2011.6130370openalex-snapshot
Detecting and tracking people using an RGB-D camera via multiple detector fusion
inferredcites →https://doi.org/10.1109/icra.2014.6907688openalex-snapshot
Real-time RGB-D based people detection and tracking for mobile robots and head-worn cameras
inferredcites →https://doi.org/10.1109/icra.2015.7139363openalex-snapshot
RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features
inferredcites →https://doi.org/10.1109/ipta.2016.7820997openalex-snapshot
How to reach top accuracy for a visual pedestrian warning system from a car?
inferredcites →https://doi.org/10.1109/iros.2011.6095074openalex-snapshot
People detection in RGB-D data
inferredcites →https://doi.org/10.1109/iros.2015.7353446openalex-snapshot
Multimodal deep learning for robust RGB-D object recognition
inferredcites →https://doi.org/10.1109/tpami.2007.1166openalex-snapshot
Stereo Processing by Semiglobal Matching and Mutual Information
inferredcites →https://doi.org/10.1109/tpami.2014.2300479openalex-snapshot
Fast Feature Pyramids for Object Detection
inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.23919/mva.2017.7986862openalex-snapshot
Detecting humans in RGB-D data with CNNs
inferredcites →https://doi.org/10.5244/c.30.73openalex-snapshot
Multispectral Deep Neural Networks for Pedestrian Detection
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