Omkar2118/datasets huggingface dataset:
BEE23
Full name: Multi-bee Tracking Benchmark

We collected 32 videos that record bee colony activity from different periods on several sunny days.
The total size of the dataset is 3,562 frames and 43,169 annotations.

Introducing paper: TOPIC: A Parallel Association Paradigm for Multi-Object Tracking under Complex Motions and Diverse Scenes
GMOT-40
Full name: Generic Multiple Object Tracking (GMOT)

GMOT-40 is the first public dense dataset for Generic Multiple Object Tracking (GMOT). It contains 40 carefully annotated sequences evenly distributed among 10 object categories. Beyond the data, a challenging protocal, one-shot GMOT, is adopted and a series of baseline algorithms is introduced. GMOT-40 is featured in

Dense objects. Over 80 objects of the same class could appear in one frame.
High-quality label. Manual annotation with careful inspection in each frame.
Diversity in target. Large variability between classes and within the sequences of the same class.
Real world challenges. Occlusion, target enter/exiting, motion blur, deformation and so on.
Challenging protocol. One-shot GMOT protocol is adopted for evaluation.
New baselines. A series of baseline methods for one-shot GMOT task is introduced.

Introducing paper: GMOT-40: A Benchmark for Generic Multiple Object Tracking
NeRF-MVL
Full name: object-centric multi-view LiDAR dataset

We establish an object-centric **m**ulti-**v**iew **L**iDAR dataset, which we
dub the **NeRF-MVL** dataset, containing carefully calibrated sensor poses,
acquired from multi-LiDAR sensor data from real autonomous vehicles. It contains
more than **76k frames** covering two types of collecting vehicles, three LiDAR
settings, two collecting paths, and nine object categories.

Introducing paper: LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields
EchoNet LVH

Echocardiography, or cardiac ultrasound, is the most widely used and readily available imaging modality to assess cardiac function and structure. Combining portable instrumentation, rapid image acquisition, high temporal resolution, and without the risks of ionizing radiation, echocardiography is one of the most frequently utilized imaging studies in the United States and serves as the backbone of cardiovascular imaging. For diseases ranging from heart failure to valvular heart diseases, echocardiography is both necessary and sufﬁcient to diagnose many cardiovascular diseases. In addition to our deep learning model, we introduce a new large video dataset of echocardiograms (parasternal long axis view) for computer vision research. The EchoNet-LVH dataset includes 12,000 labeled echocardiogram videos and human expert annotations (measurements, tracings, and calculations) to provide a baseline to study cardiac chamber size and wall thickness.