Background-subtraction using contour-based fusion of thermal and visible imagery

By James W. Davis, Vinay Sharma. 2007

433 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

LLVIP: A Visible-infrared Paired Dataset for Low-light Vision (2021, background): “TNO [21], KAIST Multispectral Dataset [7], OTCBVS OSU Color-Thermal Database [3], etc. are all very practical datasets.”
Thermal cameras and applications: a survey (2013, background): “eral purpose human detection systems are proposed in [81,36,38,174].”
A Thermal Infrared Video Benchmark for Visual Analysis (2014, neutral): “OTCBVS OSU Pedestrian [6] 360× 240 10 284 Pedestrian Sparse 1 (thermal) OSU Color-Thermal [7] 320× 240 6 17,089 Pedestrian Sparse 1 (thermal) + 1 (visible) IRIS Face 320× 240 N/A 8,456 Face N/A 1 (thermal) + 1 (visible) Terravic Face 320× 240 20 23,335 Face N/A 1 (thermal) Terravic Motion 320× 240 1”
Visible and Infrared Image Fusion Using Deep Learning (2023, methodology): “Moreover, the OSU dataset [185] is an earlier dataset used in VIF.”
LasHeR: A Large-Scale High-Diversity Benchmark for RGBT Tracking (2021, background): “Early works develop some smallscale datasets including OSU-CT [5] and LITIV [6], which contain six and nine RGBT videos respectively.”
Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery (2013, methodology): “To prove the generality of our framework, a ﬁnal test has also been conducted on a public dataset of a totally different scenario, which is an outdoor scene from the OSU Color/Thermal database [11].”
Getting to Know Low-light Images with The Exclusively Dark Dataset (2018, methodology): “Datasets such as the OTCVBS ([22], [27], [20], [28]), LSI ([29]), and LDHF ([21]) were acquired with careful setup and sophisticated hardware that is much more difﬁcult to achieve compared to visible light images taken by common digital cameras.”
Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking (2016, background): “are 4 videos from OSU Color-Thermal [5] and LITIV [6].”
RGB-T Object Tracking: Benchmark and Baseline (2018, background): “OSU-CT dataset [4] contains 6 RGB-T video sequence pairs recorded from two different locations with only people moving, which is obviously not sufficient to evaluate tracking algorithms.”