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A list of resouces for multispectral pedestrian detection,including the datasets, methods, annotations and tools.

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Multispectral Pedestrian Detection Resource

A list of resouces for multispectral pedestrian detection,including the datasets, methods, annotations and tools.

Datasets

  • KAIST dataset: The KAIST Multispectral Pedestrian Dataset consists of 95k color-thermal pairs (640x480, 20Hz) taken from a vehicle. All the pairs are manually annotated (person, people, cyclist) for the total of 103,128 dense annotations and 1,182 unique pedestrians. The annotation includes temporal correspondence between bounding boxes like Caltech Pedestrian Dataset.
  • CVC-14 dataset: The CVC-14 dataset is composed by two sets of sequences. These sequences are named as the day and night sets, which refers to the moment of the day they were acquired, and Visible and FIR depending the camera that was user to recor the sequences. For training 3695 images during the day, and 3390 images during night, with around 1500 mandatory pedestrian annotated for each sequence. For testing around 700 images for both sequences with around 2000 pedestrian during day, and around 1500 pedestrian during night.
  • FLIR dataset: Synced annotated thermal imagery and non-annotated RGB imagery for reference. It should to noted that the infrared and RGB images are not aligned. The FLIR dataset has 10,228 total frames and 9,214 frames with bounding boxes.(28151 Person, 46692 Car, 4457 Bicycle, 240 Dog, 2228 Other Vehicle)

Methods

  • Multispectral Pedestrian Detection Benchmark Dataset and Baseline, 2015, Soonmin Hwang et al. [PDF] [Code]

  • Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks, 2016, Jörg Wagner et al. [PDF]

  • Multispectral Deep Neural Networks for Pedestrian Detection, 2016, Jingjing Liu et al. [PDF] [Code]

  • Multi-spectral Pedestrian Detection Based on Accumulated Object Proposal with Fully Convolutional Networks, 2016, Hangil Choi et al. [PDF]

  • Fully Convolutional Region Proposal Networks for Multispectral Person Detection, 2017, Daniel König et al. [PDF]

  • Unified Multi-spectral Pedestrian Detection Based on Probabilistic Fusion Networks, 2017, Kihong Park et al. [PDF]

  • Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection, 2018, Dayan Guan et al. [PDF] [Code]

  • Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection, BMVC 2018, Chengyang Li et al. [PDF] [Code]

  • Pedestrian detection at night by using Faster R-CNN infrared images, 2018, Michelle Galarza Bravo et al. [PDF]

  • Real-Time Multispectral Pedestrian Detection with a Single-Pass Deep Neural Network, 2018, Maarten Vandersteegen et al. [PDF]

  • Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation, BMVC 2018, Chengyang Li et al. [PDF] [Code] [Project Link]

  • Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pesdestrian Detecion, 2019, Yanpeng Cao et al. [PDF] [Code]

  • Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection, ICCV 2019, Lu Zhang et al. [PDF] [Code]

  • The Cross-Modality Disparity Problem in Multispectral Pedestrian Detection, 2019, Lu Zhang et al. [PDF]

  • Cross-modality interactive attention network for multispectral pedestrian, 2019, Lu Zhang et al. [PDF] [Code]

  • GFD-SSD Gated Fusion Double SSD for Multispectral Pedestrian Detection, 2019, Yang Zheng et al. [PDF]

  • Unsupervised Domain Adaptation for Multispectral Pedestrian Detection, 2019, Dayan Guan et al. [PDF] [Code]

  • Generalization ability of region proposal networks for multispectral person detection, 2019, Kevin Fritz et al.[PDF]

  • Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery, 2019, Chaitanya Devaguptapu et al. [PDF]

  • Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks, ICIP 2020, Heng Zhang et al. [PDF]

  • Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems, ECCV 2020, Kailai Zhou et al. [PDF][Code]

  • Task-conditioned Domain Adaptation for Pedestrian Detection in Thermal Imagery, ECCV 2020, My Kieu et al. [PDF]

  • Anchor-free Small-scale Multispectral Pedestrian Detection, BMVC 2020, Alexander Wolpert et al. [PDF][Code]

  • Robust pedestrian detection in thermal imagery using synthesized images, ICPR 2020, My Kieu et al.[PDF]

  • Pixel Invisibility: Detecting Objects Invisible in Color Image, Yongxin Wang et al.[PDF]

  • Guided Attentive Feature Fusion for Multispectral Pedestrian Detection, WACV 2021, Heng Zhang et al. [PDF]

  • Deep Active Learning from Multispectral Data Through Cross-Modality Prediction Inconsistency, ICIP2021, Heng Zhang et al. [PDF]

  • Spatio-Contextual Deep Network Based Multimodal Pedestrian Detection For Autonomous Driving, Kinjal Dasgupta et al. [PDF]

  • Uncertainty-Guided Cross-Modal Learning for Robust Multispectral Pedestrian Detection, IEEE Transactions on Circuits and Systems for Video Technology 2021, Jung Uk Kim et al. [PDF]

  • [survey] From handcrafted to deep features for pedestrian detection: a survey, IEEE Transactions on Pattern Analysis and Machine Intelligence 2021, Jiale Cao et al. [PDF]

Improved KAIST Annotations

Tools

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A list of resouces for multispectral pedestrian detection,including the datasets, methods, annotations and tools.