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 method for end-to-end object tracking by learning reinforced attentional representation.

Keyphrases: object tracking video

Dataset description:
VOT2017: VOT2017 is a Visual Object Tracking dataset for different tasks that contains 60 short sequences annotated with 6 different attributes.
EyeCar: EyeCar is a dataset of driving videos of vehicles involved in rear-end collisions paired with eye fixation data captured from human subjects. It contains 21 front-view videos that were captured in various traffic, weather, and day light conditions.
DDD20: The dataset was captured with a DAVIS camera that concurrently streams both dynamic vision sensor (DVS) brightness change events and active pixel sensor (APS) intensity frames. DDD20 is the longest event camera end-to-end driving dataset to date with 51h of DAVIS event+frame camera and vehicle human control data collected from 4000km of highway and urban driving under a variety of lighting conditions.
VOT2016: VOT2016 is a video dataset for visual object tracking. It contains 60 video clips and 21,646 corresponding ground truth maps with pixel-wise annotation of salient objects.
EVE: EVE (End-to-end Video-based Eye-tracking) is a dataset for eye-tracking. It is collected from 54 participants and consists of 4 camera views, over 12 million frames and 1327 unique visual stimuli (images, video, text), adding up to approximately 105 hours of video data in total.

Options:- A: VOT2017  B: EyeCar  C: DDD20  D: VOT2016  E: EVE
["VOT2017", "VOT2016"]