PT Unknown
AU Aneesh Rangnekar
   Zachary Mulhollan
   Anthony Vodacek
   Matthew Hoffman
   Angel Sappa
   Erik Blasch
   Jun Yu
   Liwen Zhang
   Shenshen Du
   Hao Chang
   Keda Lu
   Zhong Zhang
   Fang Gao
   Ye Yu
   Feng Shuang
   Lei Wang
   Qiang Ling
   Pranjay Shyam
   Kuk-Jin Yoon
   Kyung-Soo Kim
TI Semi-Supervised Hyperspectral Object Detection Challenge Results – PBVS 2022
BT IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
PY 2022
BP 390
EP 398
DI 10.1109/CVPRW56347.2022.00054
DE Training; Computer visio; Conferences; Training data; Object detection; Semisupervised learning; Transformers
AB This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the Computer Vision and Pattern Recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results.
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