78 machine learning datasets
78 dataset results
This is an example data set for a hypothetical electronic products supply network.
We present a new simulated dataset for pedestrian action anticipation collected using the CARLA simulator. To generate this dataset, we place a camera sensor on the ego-vehicle in the Carla environment and set the parameters to those of the camera used to record the PIE dataset (i.e., 1920x1080, 110° FOV). Then, we compute bounding boxes for each pedestrian interacting with the ego vehicle as seen through the camera's field of view. We generated the data in two urban environments available in the CARLA simulator: Town02 and Town03.
This dataset contains Axivity AX3 wrist-worn activity tracker data that were collected from 151 participants in 2014-2016 around the Oxfordshire area. Participants were asked to wear the device in daily living for a period of roughly 24 hours, amounting to a total of almost 4,000 hours. Vicon Autograph wearable cameras and Whitehall II sleep diaries were used to obtain the ground truth activities performed during the period (e.g. sitting watching TV, walking the dog, washing dishes, sleeping), resulting in more than 2,500 hours of labelled data. Accompanying code to analyse this data is available at https://github.com/activityMonitoring/capture24. The following papers describe the data collection protocol in full: i.) Gershuny J, Harms T, Doherty A, Thomas E, Milton K, Kelly P, Foster C (2020) Testing self-report time-use diaries against objective instruments in real time. Sociological Methodology doi: 10.1177/0081175019884591; ii.) Willetts M, Hollowell S, Aslett L, Holmes C, Doherty
The SoccerTrack dataset comprises top-view and wide-view video footage annotated with bounding boxes. GNSS coordinates of each player are also provided. We hope that the SoccerTrack dataset will help advance the state of the art in multi-object tracking, especially in team sports.
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This data set contains over 600GB of multimodal data from a Mars analog mission, including accurate 6DoF outdoor ground truth, indoor-outdoor transitions with continuous cross-domain ground truth, and indoor data with Optitrack measurements as ground truth. With 26 flights and a combined distance of 2.5km, this data set provides you with various distinct challenges for testing and proofing your algorithms. The UAV carries 18 sensors, including a high-resolution navigation camera and a stereo camera with an overlapping field of view, two RTK GNSS sensors with centimeter accuracy, as well as three IMUs, placed at strategic locations: Hardware dampened at the center, off-center with a lever arm, and a 1kHz IMU rigidly attached to the UAV (in case you want to work with unfiltered data). The sensors are fully pre-calibrated, and the data set is ready to use. However, if you want to use your own calibration algorithms, then the raw calibration data is also ready for download. The cross-domai
The Robot Tracking Benchmark (RTB) is a synthetic dataset that facilitates the quantitative evaluation of 3D tracking algorithms for multi-body objects. It was created using the procedural rendering pipeline BlenderProc. The dataset contains photo-realistic sequences with HDRi lighting and physically-based materials. Perfect ground truth annotations for camera and robot trajectories are provided in the BOP format. Many physical effects, such as motion blur, rolling shutter, and camera shaking, are accurately modeled to reflect real-world conditions. For each frame, four depth qualities exist to simulate sensors with different characteristics. While the first quality provides perfect ground truth, the second considers measurements with the distance-dependent noise characteristics of the Azure Kinect time-of-flight sensor. Finally, for the third and fourth quality, two stereo RGB images with and without a pattern from a simulated dot projector were rendered. Depth images were then recons
IoT-23 is a dataset of network traffic from Internet of Things (IoT) devices. It has 20 malware captures executed in IoT devices, and 3 captures for benign IoT devices traffic. It was first published in January 2020, with captures ranging from 2018 to 2019. These IoT network traffic was captured in the Stratosphere Laboratory, AIC group, FEL, CTU University, Czech Republic. Its goal is to offer a large dataset of real and labeled IoT malware infections and IoT benign traffic for researchers to develop machine learning algorithms. This dataset and its research was funded by Avast Software. The malware was allow to connect to the Internet.
The data set contains point cloud data captured in an indoor environment with precise localization and ground truth mapping information. Two ”stop-and-go” data sequences of a robot with mounted Ouster OS1-128 lidar are provided. This data-capturing strategy allows recording lidar scans that do not suffer from an error caused by sensor movement. Individual scans from static robot positions are recorded. Additionally, point clouds recorded with the Leica BLK360 scanner are provided as mapping ground-truth data.
This package contains an anonymized packets of 802.11 probe requests captured throughout March of 2023 at Universitat Jaume I. The packet capture file is in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).
The EyeInfo Dataset is an open-source eye-tracking dataset created by Fabricio Batista Narcizo, a research scientist at the IT University of Copenhagen (ITU) and GN Audio A/S (Jabra), Denmark. This dataset was introduced in the paper "High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking Methods" (DOI: 10.3390/vision5030041). The dataset contains high-speed monocular eye-tracking data from an off-the-shelf remote eye tracker using active illumination. The data from each user has a text file with data annotations of eye features, environment, viewed targets, and facial features. This dataset follows the principles of the General Data Protection Regulation (GDPR).
Understanding comprehensive assembly knowledge from videos is critical for futuristic ultra-intelligent industry. To enable technological breakthrough, we present HA-ViD – an assembly video dataset that features representative industrial assembly scenarios, natural procedural knowledge acquisition process, and consistent human-robot shared annotations. Specifically, HA-ViD captures diverse collaboration patterns of real-world assembly, natural human behaviors and learning progression during assembly, and granulate action annotations to subject, action verb, manipulated object, target object, and tool. We provide 3222 multi-view and multi-modality videos, 1.5M frames, 96K temporal labels and 2M spatial labels. We benchmark four foundational video understanding tasks: action recognition, action segmentation, object detection and multi-object tracking. Importantly, we analyze their performance and the further reasoning steps for comprehending knowledge in assembly progress, process effici
Abstract This data set is a data set used for aircraft theoretical velocity ranking. Four sensors are randomly arranged in a 1*1 square map, and three aircraft will fly over the map coverage area at the same time. The velocity of the aircraft is simulated by a random process. The theoretical velocities of the three aircraft are similar, and the velocity of the aircraft will be disturbed during actual flight, causing large fluctuations, so that it is difficult to distinguish the theoretical velocity order of the aircraft flying into the map. The coverage area of the sensor is circular with a fixed radius. The four sensors have a unified detection interval event and will detect the position of the aircraft within the coverage area with unified accuracy. The target task is to reason the theoretical velocity ranking of three aircraft through the trajectory data collected by the sensors.
The dataset was collected from two courses offered on the University of Jordan's E-learning Portal during the second semester of 2020, namely "Computer Skills for Humanities Students" (CSHS) and "Computer Skills for Medical Students" (CSMS). Over the sixteen-week duration of each course, students participated in various activities such as reading materials, video lectures, assignments, and quizzes. To preserve student privacy, the log activity of each student was anonymized. Data was aggregated from multiple sources, including the Moodle learning management system and the student information system, and consolidated into a single database. The dataset contains information on the number of learners and events for each course, as well as their launch and end dates. CSHS had 1749 learners and 1,139,810 events from January 21, 2020 to May 20, 2020, while CSMS had 564 learners and 484,410 events during the same period. The dataset is based on the Filder and Silverman learning style model (F
Collected data from two distinct experiments in immersive, interactive VR where participants performed dynamic tasks as their eye, head, and hand movements were recorded. In the second experiment, a range of real-time privacy mechanisms are applied to eye gaze in real-time.
The uniD dataset is an innovative collection of naturalistic road user trajectories, captured within the RWTH Aachen University campus using drone technology to address common challenges such as occlusions found in traditional traffic data collection methods. It meticulously documents the movement and classifies each road user by type. Employing cutting-edge computer vision algorithms, the dataset ensures high positional accuracy. Its utility spans various applications, from predicting road user behavior and modeling driver actions to conducting scenario-based safety checks for automated driving systems and facilitating the data-driven design of Highly Automated Driving (HAD) system components.
The dataset concerns toy tasks that a human should teach to a robot. The number of task repetitions is limited in the dataset since the human should demonstrate the task to the robot only a few times.
We use Something-Something v2 dataset to obtain the generation prompts and ground truth masks from real action videos. We filter out a set of 295 prompts. The details for this filtering are in the "Peekaboo: Interactive Video Generation via Masked-Diffusion" paper. We then use an off-the-shelf OWL-ViT-large open-vocabulary object detector to obtain the bounding box (bbox) annotations of the object in the videos. This set represents bbox and prompt pairs of real-world videos, serving as a test bed for both the quality and control of methods for generating realistic videos with spatio-temporal control.
This is a dataset of robot motions based on physics simulations.
This is a comprehensive dataset of human arm motion during Activities of Daily Living (ADL). The Cartesian locations of the head, torso, and arm segments were recorded using a motion capture system (Vicon) from 12 participants (ages 18-72, 6 male, 6 female) performing 24 unique tasks. These include both standing and sitting tasks, as well as repetitions, selected based on what would be most useful for prosthesis users, resulting in 72 recorded trials per subject. Dataset was collected and analyzed for identification, categorization, and simplification, of movement patterns for upper-limb prosthesis control in [Gloumakov Y, Spiers AJ, Dollar AM, “Dimensionality Reduction and Motion Clustering During Activities of Daily Living: Three-, Four-, and Seven-Degree-of-Freedom Arm Movements,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020] and in [Gloumakov Y, Spiers AJ, Dollar AM, “Dimensionality Reduction and Motion Clustering during Activities of Daily Living: Decou