Given a short query with keyphrases about a research idea in 2019 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: I'm interested in developing a convolutional neural network that can estimate human body joints.

Keyphrases: pose estimation images

Dataset description:
COCO: The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.
LOCATA: The LOCATA dataset is a dataset for acoustic source localization. It consists of real-world ambisonic speech recordings with optically tracked azimuth-elevation labels.
MSRC-12: The Microsoft Research Cambridge-12 Kinect gesture data set consists of sequences of human movements, represented as body-part locations, and the associated gesture to be recognized by the system. The data set includes 594 sequences and 719,359 frames—approximately six hours and 40 minutes—collected from 30 people performing 12 gestures.
ITOP: The ITOP dataset consists of 40K training and 10K testing depth images for each of the front-view and top-view tracks. This dataset contains depth images with 20 actors who perform 15 sequences each and is recorded by two Asus Xtion Pro cameras.

Options:- A: COCO  B: LOCATA  C: MSRC-12  D: ITOP
COCO