People detection and tracking from aerial thermal views

By JÃ¼rg Portmann, Simon Lynen, Margarita Chli, Roland Yves Siegwart. 2014

187 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Multispectral pedestrian detection: Benchmark dataset and baseline (2015, methodology): “2, we developed imaging hardware consisting of a color camera, a thermal camera, a beam splitter, and a three-axis camera jig.”
RGB-T Object Tracking: Benchmark and Baseline (2018, unknown): “VOT2015[20] 60 21.8K 1.5K X X X 2015 Temple-Color[21] 128 55.3K 3.8K X X X 2015 ALOV++[22] 315 15.1K 5.9K X X X 2013 NUS-PRO[23] 365 135.8K 5K X X X 2016 Thermal OSU-T[24] 10 0.2K - X X - 2005 ASL-TID[25] 9 4.3k - - - - 2014 TIV[26] 16 63K 5.9K - - - 2014 LTIR[27] 20 11.2K 1.4K X X X 2015 RGB-T OSU-”
Computer vision and deep learning techniques for pedestrian detection and tracking: A survey (2018, background): “Recent literature contains several works dealing with marker-based human tracking, the most of which make use of drones [51,65,77–79, 91, 94, 96,”
Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications (2017, background): “There are also more specialized trackers tailored to address speciﬁc problems and unique camera systems such as in wide aerial video ([41; 45]), thermal and IR video ([13; 43]), and RGB-D video ([36]).”
A Thermal Infrared Video Benchmark for Visual Analysis (2014, methodology): “A few of thermal infrared dataset have been published in the past, e.g., the OTCBVS Benchmark 1, the LITIV Thermal-Visible Registration Dataset [3], the AIC ThermalVisible Night-time Dataset [4], and the ASL Thermal Infrared Dataset [5] (Table I).”
Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison (2016, background): “Visual pedestrian detection has received attention for more than a decade from computer vision researchers due to its multiple applications in Advance Driver Assistance Systems (ADAS) [1–3], autonomous vehicles [4] and video surveillance [5–7], being nowadays still a challenging problem.”
A Benchmark and Simulator for UAV Tracking (2016, background): “There are also more specialized trackers tailored to address specific problems and unique camera systems such as in wide aerial video [34,36], thermal and IR video [10,35], and RGB-D video [29].”