The Waymo Open Dataset is comprised of high resolution sensor data collected by autonomous vehicles operated by the Waymo Driver in a wide variety of conditions. The Waymo Open Dataset currently contains 1,950 segments. The authors plan to grow this dataset in the future. Currently the datasets includes: * 1,950 segments of 20s each, collected at 10Hz (390,000 frames) in diverse geographies and conditions * Sensor data * 1 mid-range lidar * 4 short-range lidars * 5 cameras (front and sides) * Synchronized lidar and camera data * Lidar to camera projections * Sensor calibrations and vehicle poses * Labeled data * Labels for 4 object classes - Vehicles, Pedestrians, Cyclists, Signs * High-quality labels for lidar data in 1,200 segments * 12.6M 3D bounding box labels with tracking IDs on lidar data * High-quality labels for camera data in 1,000 segments * 11.8M 2D bounding box labels with tracking IDs on camera data

The EPIC-KITCHENS-55 dataset comprises a set of 432 egocentric videos recorded by 32 participants in their kitchens at 60fps with a head mounted camera. There is no guiding script for the participants who freely perform activities in kitchens related to cooking, food preparation or washing up among others. Each video is split into short action segments (mean duration is 3.7s) with specific start and end times and a verb and noun annotation describing the action (e.g. ‘open fridge‘). The verb classes are 125 and the noun classes 331. The dataset is divided into one train and two test splits. Source: Egocentric Hand Track and Object-based Human Action Recognition Image Source: https://epic-kitchens.github.io/2020-100

ImageNet VID is a large-scale public dataset for video object detection and contains more than 1M frames for training and more than 100k frames for validation.

Prophesee’s GEN1 Automotive Detection Dataset is the largest Event-Based Dataset to date. The dataset was recorded using a PROPHESEE GEN1 sensor with a resolution of 304×240 pixels, mounted on a car dashboard. The labels were obtained using the gray level estimation feature of the ATIS camera by labelling manually. It contains 39 hours of open road and various driving scenarios ranging from urban, highway, suburbs and countryside scenes. Manual bounding box annotations are available for two classes are present: pedestrians and cars. (Truck and buses are not labelled).

Are current 3D object tracking methods truely robust enough for low-fidelity depth sensors like the iPhone LiDAR? We introduce DTTD-Mobile (fully compatible w/ YCB toolbox), a new benchmark built on real-world data captured from mobile devices; 18 objects observed in 100 videos with 47,668 sampled frames and 114,143 object annotations. We evaluate several popular methods—including BundleSDF, ES6D, MegaPose, and DenseFusion—and highlight their limitations in this challenging setting. Keywords: ObjectPoseEstimation, MobileAI, EdgeAI, ARVR, CVPR

YouTube-BoundingBoxes (YT-BB) is a large-scale data set of video URLs with densely-sampled object bounding box annotations. The data set consists of approximately 380,000 video segments about 19s long, automatically selected to feature objects in natural settings without editing or post-processing, with a recording quality often akin to that of a hand-held cell phone camera. The objects represent a subset of the MS COCO label set. All video segments were human-annotated with high-precision classification labels and bounding boxes at 1 frame per second. Source: YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video

OAK is a dataset for online continual object detection benchmark with an egocentric video dataset. OAK adopts the KrishnaCam videos, an ego-centric video stream collected over nine months by a graduate student. OAK provides exhaustive bounding box annotations of 80 video snippets (~17.5 hours) for 105 object categories in outdoor scenes.

Specially designed to evaluate active learning for video object detection in road scenes. Source: Temporal Coherence for Active Learning in Videos

Description： 5,011 Images – Human Frontal face Data (Male). The data diversity includes multiple scenes, multiple ages and multiple races. This dataset includes 2,004 Caucasians , 3,007 Asians. This dataset can be used for tasks such as face detection, race detection, age detection, beard category classification. Data size： 5,011 people, one image per person Race distribution： 2,004 Caucasians , 3,007 Asians

Underwater Trash Detection Dataset ## Overview The Underwater Trash Detection Dataset is a custom-annotated dataset designed to address the challenges of underwater trash detection caused by varying environmental features. Publicly available datasets alone are insufficient for training deep learning models due to domain-specific variations in underwater conditions. This dataset offers a cumulative, self-annotated collection of underwater images for detecting and classifying trash, providing a strong foundation for deep learning research and benchmark testing. --- ## Dataset Summary - Total Images: 9,576 - Annotation Types: Trash classification (plastic, trash, underwater debris) vs. environmental factors (fish, flora, fauna). --- ## Dataset Split | Split | Percentage | Number of Images | |-------------|----------------|-----------------------| | Train Set | 76% | 7,308 | | Validation | 19% | 1,795 | | Test Set | 5% | 473 | --- ## Preprocessing - Image Resize: All images are resized to 256x256 pixels using stretching for uniform input dimensions. --- ## Purpose This dataset supports research in underwater trash detection while addressing storage and computational constraints in underwater mobile devices. It enables the development of optimized algorithms for efficient trash detection and classification using minimal resources. --- ## Citation If you use this dataset in your research, please cite: ```bibtex @InProceedings{10.1007/978-3-031-43360-3_24, author="Walia, Jaskaran Singh and Seemakurthy, Karthik", editor="Iida, Fumiya and Maiolino, Perla and Abdulali, Arsen and Wang, Mingfeng", title="Optimized Custom Dataset for Efficient Detection of Underwater Trash", booktitle="Towards Autonomous Robotic Systems", year="2023", publisher="Springer Nature Switzerland", address="Cham", pages="292--303", }

Temporal Hands Guns and Phones (THGP) dataset, is a collection of 5960 video frames (5000 for training and 960 for testing). The training part is composed with 50 videos of 100 frames (720 × 720 pixels). This dataset contains 20 videos of shooting drills, 20 videos of armed robberies, and 10 videos of people making calls. The testing part contains 48 videos of 20 frames (720 × 720). Videos contained in the testing dataset includes phone calls, gun reviews, shooting drills, people making calls, and armed robberies at convenience stores. This dataset is labeled with the bounding boxes of hands, phones, and guns.

USC-GRAD-STDdb comprises 115 video segments containing more than 25,000 annotated frames of HD 720p resolution (≈1280x720) with small objects of interest from 16 (≈4x4) to 256 (≈16x16) as pixel area. The length of the videos changes from 150 up to 500 frames. The size of every object is determined through the bounding box, so that a good annotation is of utmost importance for reliable performance metrics. As it may seem obvious, the smaller the object, the harder the annotation. The annotation has been carried out with the ViTBAT tool, adjusting the boxes as much as possible to the objects of interest in each video frame. In total, more than 56,000 ground truth labels have been generated.

VISEM-Tracking is a dataset consisting of 20 video recordings of 30s of spermatozoa with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. It is an extension of the previously published VISEM dataset. In addition to the annotated data, unlabeled video clips are provided for easy-to-use access and analysis of the data. Source: VISEM-Tracking: Human Spermatozoa Tracking Dataset Image Source: https://arxiv.org/pdf/2212.02842v1.pdf