Workshop and Challenge: Automated Analysis of Marine Video for Environmental Monitoring
Data Set Download Links
All data currently released for the challenge is listed below. The training data is meant for the performers to train algorithms on.
There are two tar balls:
Workshop and Challenge: Automated Analysis of Marine Video for Environmental Monitoring
Data Set Download Links
All data currently released for the challenge is listed below. The training data is meant for the performers to train algorithms on.
There are two tar balls: one containing the imagery and another containing the truth for those images.
The data releases are comprised of images and annotations from five different data sources, with six datasets in total.
- HabCam: abcam_seq0
- MOUSS: mouss_seq0, mouss_seq1
- AFSC DropCam: afsc_seq0
- MBARI: mbari_seq0
- NWFSC: nwfsc_seq0
Each dataset contains different types of imagery — different lighting conditions, camera angles, and wildlife. The data released depends on the nature of the data in the entire dataset. The following gallery contains an example image from each dataset.
The HabCam imagery is collected from a down-looking, RGB camera about 1.5m above the ocean floor. The platform includes synchronized flashes near the cameras, as there is no ambient illumination at the collection depths (>200m). The images tend to show scallops, sand dollars, rocks, sand and the occasional fish. The annotations include scallops and fish. The initial dataset has 10465 images. The images are 2720×1024. Note that habcam imagery was captured in stereo. Each image in this dataset contains both the left and right camera stacked horizontally. Annotations are only provided for the left camera.
The MOUSS data is collected from a stationary, horizontal, grayscale camera 1-2 meters above the ocean floor, using ambient illumination. Typically the camera is on the bottom for 15 minutes in each position. The initial data is from three such collections, consisting of 159 images with species labels on all fish close enough to the camera to be identified by a human expert. Fish that are smaller than this (about 30 pixels in length) will not be disregarded in scoring, i.e. missing them will not count against recall and detecting them will not count as false alarms. The test data without released annotations will include images from the training collections as well as novel collections. The images have a resolution of 968×728.
The AFSC data is collected from an underwater remotely operated vehicle (ROV) equipped with an RGB video camera looking horizontally. The overall dataset consists of a number of videos from different ROV missions. Because the platform moves slowly, some of the images have a fair amount of spatial overlap; such groups of images are called “clusters”. The released data is randomly sampled from some of the clusters, while other clusters are sequestered and will only be released in the test data. There are 571 images in this dataset. The image resolution is 2112×2816.
The MBARI dataset was collected by the Monterey Bay Aquariam Research Institute. It constains a a single video consisting of 666 RGB frames. Each image has a 1920×1080 resolution.
The NWFSC data was also collected from an ROV, but looking downward at the ocean floor. The spatial overlap in this collection is minimal, and the released data is randomly sampled from the overall set. There are 123 images in the initial release and these images have a resolution of 2448×2050. The annotations in this dataset are actually keypoints instead of bounding boxes.
Ground Truth Annotation Format
The annotations are formatted according to the MSCOCO standard. This is a JSON-compliant format which contains the images included, the annotations for those images, and the categories for those annotations.
See Sections 1 and 2 on the official documentation page for more information.
Results Annotation Format
The result annotations should be formatted according to the MSCOCO results standard. In particular, detections s