Temporal Action Localization

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ANUBIS is a large-scale human skeleton dataset containing 80 actions. Compared with previously collected datasets, ANUBIS is advantageous in the following four aspects: (1) employing more recently released sensors; (2) containing novel back view; (3) encouraging high enthusiasm of subjects; (4) including actions of the COVID pandemic era.

CAShift is the first multiple normality shift-aware Log-Based Anomaly Detection (LAD) dataset specifically designed for cloud systems, which considers different software roles in cloud systems and attack behavior among cloud components.

Daily Activity Recordings for Artificial Intelligence (DARai, pronounced "Dahr-ree") is a multimodal, hierarchically annotated dataset constructed to understand human activities in real-world settings. DARai consists of continuous scripted and unscripted recordings of 50 participants in 10 different environments, totaling over 200 hours of data from 20 sensors including multiple camera views, depth and radar sensors, wearable inertial measurement units (IMUs), electromyography (EMG), insole pressure sensors, biomonitor sensors, and gaze tracker. To capture the complexity in human activities, DARai is annotated at three levels of hierarchy: (i) high-level activities (L1) that are independent tasks, (ii) lower-level actions (L2) that are patterns shared between activities, and (iii) fine-grained procedures (L3) that detail the exact execution steps for actions. The dataset annotations and recordings are designed so that 22.7% of L2 actions are shared between L1 activities and 14.2% of L3 procedures are shared between L2 actions. The overlap and unscripted nature of DARai allows counterfactual activities in the dataset. Experiments with various machine learning models showcase the value of DARai in uncovering important challenges in human-centered applications. Specifically, we conduct unimodal and multimodal sensor fusion experiments for recognition, temporal localization, and future action anticipation across all hierarchical annotation levels. To highlight the limitations of individual sensors, we also conduct domain-variant experiments that are enabled by DARai's multi-sensor and counterfactual activity design setup. The code, documentation, and dataset are available at the dedicated DARai website.

This dataset is a result of a study that was created to assess drivers behaviors when following a lead vehicle. The driving simulator study used a simulated suburban environment for collecting driver behavior data while following a lead vehicle driving through various unsignalized intersections. The driving environment had two lanes in each direction and a dedicated left-turn lane for the intersection. The experiment was deployed on a miniSim Driving Simulator. We programmed the lead vehicle ran- domly turn left, right or go straight through the intersections. In total we had 2(traffic density) × 2(speed level) × 3 = 12 scenarios for each participant to be tested on. We split the data into train, validation and test sets. The setup for the task is to observe 1 second of trajectories and predict the next 3,5 and 8 seconds.

The Freiburg Street Crossing dataset consists of data collected from three different street crossings in Freiburg, Germany; ; two of which were traffic light regulated intersections and one a zebra crossing without traffic lights. The data can be used to train agents to cross roads autonomously. Source: http://aisdatasets.informatik.uni-freiburg.de/streetcrossing/ Image Source: http://aisdatasets.informatik.uni-freiburg.de/streetcrossing/

Metaphorics is a newly introduced non-contextual skeleton action dataset. All the datasets introduced so far in the skeleton human action recognition have categories based only on verb-based actions. Source: Metaphorics

Structured atmospheric data for AI/ML Long-term, pre-processed, atmospheric datasets for use in Machine Learning/AI based forecasting. Initially intended to predict AOD, however can be adapted for prediction of other atmospheric particles. Machine Learning ready NumPy* dataset constructed by pre-processing selected atmospheric variables at 5 pressure levels form ERA5 reanalysis (resulting in 35 features) and AOD data from MODIS on board of Aqua and Terra satellites. This is a long-term daily dataset which spans 20 years from 1st Jan 2003 to 31st Dec 2022 and is homogeneously structured into 1ºx1º grid cells. Missing days and AOD values from MODIS were imputed using Lattice Kriging method (Python code used for imputation included as Jupyter Notebook 'Combine_impute_AOD.ipynb'), but raw (unimputed) MODIS data are also available. All datasets were created for a purpose of training Convolutional Neural Network model designed to forecast Saharan dust (DustNet). These datasets can also be used to train other ML models, or indeed to forecast other variables. This dataset was used to train the DustNet model and predict 24-hr ahead AOD. Please see doi: 10.5281/zenodo.10722953 for further details on predicting AOD and the DustNet model code. *datasets are NumPy arrays (v1.23) created in Python v3.8.18.

The sports industry is witnessing an increasing trend of utilizing multiple synchronized sensors for player data collection, enabling personalized training systems with multi-perspective real-time feedback. Badminton could benefit from these various sensors, but there is a scarcity of comprehensive badminton action datasets for analysis and training feedback. Addressing this gap, this paper introduces a multi-sensor badminton dataset for forehand clear and backhand drive strokes, based on interviews with coaches for optimal usability. The dataset covers various skill levels, including beginners, intermediates, and experts, providing resources for understanding biomechanics across skill levels. It encompasses 7,763 badminton swing data from 25 players, featuring sensor data on eye tracking, body tracking, muscle signals, and foot pressure. The dataset also includes video recordings, detailed annotations on stroke type, skill level, sound, ball landing, and hitting location, as well as survey and interview data. We validated our dataset by applying a proof-of-concept machine learning model to all annotation data, demonstrating its comprehensive applicability in advanced badminton training and research.

We consider the task of temporal human action localization in lifestyle vlogs. We introduce a novel dataset consisting of manual annotations of temporal localization for 13,000 narrated actions in 1,200 video clips. We present an extensive analysis of this data, which allows us to better understand how the language and visual modalities interact throughout the videos. We propose a simple yet effective method to localize the narrated actions based on their expected duration. Through several experiments and analyses, we show that our method brings complementary information with respect to previous methods and leads to improvements over previous work for the task of temporal action localization.

WikiFactDiff is a dataset designed as a resource to perform atomic factual knowledge updates on language models, with the goal of aligning them with current knowledge. It describes the evolution of factual knowledge between two dates, named T_old and T_new,​ in the form of semantic triples. To enable the possibility of evaluating knowledge algorithms (such as ROME, MEND, MEMIT, etc.), these triples are verbalized and neighbor facts are determined to check for eventual bleedover.

DAHLIA dataset [1] is devoted to human activity recognition, which is a major issue for adapting smart-home services such as user assistance. DAHLIA has been realized in Mobile Mii Platform by CEA LIST, and has been partly supported by ITEA 3 Emospaces Project (https://itea3.org/project/emospaces.html) Videos were recorded in realistic conditions, with 3 Kinect v2 sensors located as they would be in a real context. The long-range activities were performed in an unconstrained way (participants received only few instructions), and in a continuous (untrimmed) sequence, resulting in long videos (40 min in average per subject). Contrary to previously published databases, in which labeled actions are very short and have low-semantic level, this new database focuses on high-level semantic activities such as « Preparing lunch » or « House Working ». [1] G. Vaquette, A. Orcesi, L. Lucat and C. Achard, "The DAily Home LIfe Activity Dataset: A High Semantic Activity Dataset for Online Recognition," 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), 2017, pp. 497-504, doi: 10.1109/FG.2017.67.

The deliberate manipulation of public opinion, especially through altered images, poses a significant danger to society. To fight this issue on a technical level we support the research community by releasing the Digital Forensics 2023 (DF2023) training and validation dataset. The DF2023 training dataset comprises one million images from four major forgery categories: * splicing (400K) * copy-move (300K) * enhancement (200K) * removal (100K) This dataset enables an objective comparison of network architectures and can significantly reduce the time and effort of researchers preparing datasets. For a detailed description of the DF2023 dataset, please refer to: @inproceedings{Fischinger2023DFNet, title={DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection}, author={David Fischinger and Martin Boyer}, journal={The 25th Irish Machine Vision and Image Processing conference. (IMVIP)}, year={2023} } available from: Zenodo ## Naming convention The naming convention of DF2023 encodes information about the applied manipulations. Each image name has the following form: COCO_DF_0123456789_NNNNNNNN.{EXT} (e.g. COCO_DF_E000G40117_00200620.jpg) After the identifier of the image data source ("COCO") and the self-reference to the Digital Forensics ("DF") dataset, there are 10 digits as placeholders for the manipulation. Position 0 defines the manipulation types copy-move, splicing, removal, enhancement ([C,S,R,E]). The following digits 1-9 represent donor patch manipulations. For positions [1,2,7,8] (resample, flip, noise and brightness), a binary value indicates if this manipulation was applied to the donor image patch. Position 3 (rotate) indicates by the values 0-3 if the rotation was executed by 0, 90, 180 or 270 degrees. Position 4 defines if BoxBlur (B) or GaussianBlur (G) was used. Position 5 specifies the blurring radius. A value of 0 indicates that no blurring was executed. Position 6 indicates which of the Python-PIL contrast filters EDGE ENHANCE, EDGE ENHANCE MORE, SHARPEN, UnsharpMask or ImageEnhance (values 1-5) was applied. If none of them was applied, this value is set to 0. Finally, position 9 is set to the JPEG compression factor modulo 10, a value of 0 indicates that no JPEG compression was applied. The 8 characters NNNNNNNN in the image name template stand for a running number of the images.

This is an image splicing dataset including different types of preprocessing and postprocessing techniques. Foreground objects are taken from HRSOD and background images are taken from BG20k datasets. 95000 train and 5000 test images are provided.

InfiniteRep is a synthetic, open-source dataset for fitness and physical therapy (PT) applications. It includes 1k videos of diverse avatars performing multiple repetitions of common exercises. It includes significant variation in the environment, lighting conditions, avatar demographics, and movement trajectories. From cadence to kinematic trajectory, each rep is done slightly differently -- just like real humans. InfiniteRep videos are accompanied by a rich set of pixel-perfect labels and annotations, including frame-specific repetition counts. The dataset features: + 100 videos per exercise, spanning 5 to 10 repetitions each (1,000 videos total) + 7 unique indoor scenes + Realistic environmental occlusion (+ corresponding labels) + Diverse lighting conditions + Varied body shape, skin tones, and clothing + Rich annotations for 2D and 3D supervision ## Exercises The dataset currently includes the following exercises: + Pushups + Alternating Bicep Curls (with dumbbells) + Delt Flys (with dumbbells) + Squats + Bird Dogs + Supermans + Bicycle Crunches + Leg Raises + Front Raises (with dumbbells) + Overhead Press (with dumbbells) ## Annotations The dataset includes the following annotations: + Bounding boxes + Segmentation masks + Keypoints + Joint angles (quaternions) + Percent occlusion + Avatar characteristics + Camera position + and more Want depth labels? They are not included in the dataset but we can send them to you. Email us at info@toinfinity.ai. ## Download Download the dataset: toinfinity.ai/infiniterep Github repo with additional documentation: https://github.com/toinfinityai/InfiniteRep ## Need more data? Infinity AI specializes in generating custom synthetic data. If you need more (or different data), drop us a line at info@toinfinity.ai (we read every email).

The University of Padova Body Pose Estimation dataset (UNIPD-BPE) is an extensive dataset for multi-sensor body pose estimation containing both single-person and multi-person sequences with up to 4 interacting people A network with 5 Microsoft Azure Kinect RGB-D cameras is exploited to record synchronized high-definition RGB and depth data of the scene from multiple viewpoints, as well as to estimate the subjects’ poses using the Azure Kinect Body Tracking SDK. Simultaneously, full-body Xsens MVN Awinda inertial suits allow obtaining accurate poses and anatomical joint angles, while also providing raw data from the 17 IMUs required by each suit. All the cameras and inertial suits are hardware synchronized, while the relative poses of each camera with respect to the inertial reference frame are calibrated before each sequence to ensure maximum overlap of the two sensing systems outputs. The setup used allowed to record synchronized 3D poses of the persons on the scene both via Xsens’ inverse kinematics algorithm (inertial motion capture) and by exploiting the Azure Kinect Body tracking SDK (markerless motion capture), simultaneously. The additional raw data (RGB, depth, camera network configuration) allow the user to assess the performance of any custom markerless motion capture algorithm (based on RGB, depth, or both). Further analyses can be progressed by varying the number of cameras being used and/or their resolution and frame rate. Moreover, raw angular velocities, linear accelerations, magnetic fields, and orientations from each IMU allow to develop and test multimodal BPE approaches focused on merging visual and inertial data. Finally, the precise body dimensions of each subject are provided. They include body height, weight, and segment lengths measured before the beginning of a recording session. They were used to scale the Xsens biomechanical model, and also constitute a ground truth for assessing the markerless BPE accuracy on estimating each subject’s body dimensions. The recorded sequences include 15 participants performing a set of 12 ADLs (e.g., walking, sitting, and jogging). The actions were chosen to present different challenges to BPE algorithms, including different movement speeds, self-occlusions, and complex body poses. Moreover, multi-person sequences, with up to 4 people performing a set of 7 different actions, are provided. Such sequences offer challenging scenarios where multiple self-occluded persons move and interact in a restricted space. They allow assessing the accuracy of multi-person tracking algorithms, focused on maintaining frame-by-frame consistent IDs of each detected person. A total of 13.3h of RGB, depth, and markerless BPE data are present in the dataset, corresponding to over 1,400,000 frames obtained from a calibrated network with 5 RGB-D cameras. The inertial suits, on the other hand, allowed to record 3h of inertial motion capture data, corresponding to a total of over 600,000 frames recorded by each of the 17 IMUs used by every suit.