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: I want to train a supervised model for pedestrian detection on a new dataset.

Keyphrases: pedestrian detection video

Dataset description:
SNIPS: The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:   SearchCreativeWork (e.g. Find me the I, Robot television show), GetWeather (e.g.
SPIRS: A  first-of-its-kind large dataset of sarcastic/non-sarcastic tweets with high-quality labels and extra features: (1) sarcasm perspective labels (2) new contextual features. The dataset is expected to advance sarcasm detection research.
Middlebury: The Middlebury Stereo dataset consists of high-resolution stereo sequences with complex geometry and pixel-accurate ground-truth disparity data. The ground-truth disparities are acquired using a novel technique that employs structured lighting and does not require the calibration of the light projectors.
FaceForensics: FaceForensics is a video dataset consisting of more than 500,000 frames containing faces from 1004 videos that can be used to study image or video forgeries. All videos are downloaded from Youtube and are cut down to short continuous clips that contain mostly frontal faces.
ConvAI2: The ConvAI2 NeurIPS competition aimed at finding approaches to creating high-quality dialogue agents capable of meaningful open domain conversation. The ConvAI2 dataset for training models is based on the PERSONA-CHAT dataset.
CrowdHuman: CrowdHuman is a large and rich-annotated human detection dataset, which contains 15,000, 4,370 and 5,000 images collected from the Internet for training, validation and testing respectively. The number is more than 10× boosted compared with previous challenging pedestrian detection dataset like CityPersons.
ETH: ETH is a dataset for pedestrian detection. The testing set contains 1,804 images in three video clips.

Options:- A: SNIPS  B: SPIRS  C: Middlebury  D: FaceForensics  E: ConvAI2  F: CrowdHuman  G: ETH
["Middlebury", "ETH"]