You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: A novel unsupervised adaptation method for pedestrian detection that leverages multiple target domains (or sub-domains) in

Keyphrases: pedestrian detection images

Dataset description:
Libri-Adapt: Libri-Adapt aims to support unsupervised domain adaptation research on speech recognition models.
EMNIST: EMNIST (extended MNIST) has 4 times more data than MNIST. It is a set of handwritten digits with a 28 x 28 format.
EPIC-KITCHENS-100: This paper introduces the pipeline to scale the largest dataset in egocentric vision EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras.
KITTI: KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner.
ETH: ETH is a dataset for pedestrian detection. The testing set contains 1,804 images in three video clips.

Options:- A: Libri-Adapt  B: EMNIST  C: EPIC-KITCHENS-100  D: KITTI  E: ETH
["KITTI", "ETH"]