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Roy-chowdhury) sdha semantic Description of Human Activities 2010 contest - aerial views (Michael. Roy-chowdhury) sfu volleyball Group Activity recognition - 2 levels annotations dataset (9 players' actions and 8 scene's activity) for volleyball videos.(M. Mori / Simon Fraser University) sysu 3d human-Object Interaction Dataset - forty subjects perform 12 distinct activities (Sun Yat-sen University) Shakefive dataset - contains only two actions, namely hand shake and high five. (Universiteit Utrecht) Shakefive2 - a dyadic human interaction dataset with limb level annotations on 8 classes in 153 hd videos(Coert van Gemeren, ronald Poppe, remco veltkamp) Sports Videos in the wild (SVW) - svw is comprised of 4200 videos captured solely with smartphones by users. 430 hours of video data and 45 million frames (Gorban, Idrees, jiang, zamir, laptev shah, sukthanka) tum kitchen Data set of everyday manipulation Activities (Moritz tenorth, jan Bandouch) tv human Interaction Dataset (Alonso patron-Perez) The falling Detection dataset - six subjects in two sceneries. (Tianjin University) ucf-iphone data set - 9 Aerobic actions were recorded from (6-9) subjects using the Inertial measurement Unit (IMU) on an Apple iphone 4 smartphone. (Corey mcCall, kishore reddy and Mubarak shah) The upcv action dataset - the dataset consists of 10 actions performed by 20 subjects twice.
Ryoo, jpl) Jena Action Recognition Dataset - aibo dog actions (Korner and Denzler) K3da - kinect 3D Active dataset - k3Da (Kinect 3D active) is a realistic clinically relevant human action dataset containing skeleton, depth data and associated participant information (D. McPhee) Kinetics Human Action Video dataset - 300,000 video clips, 400 human action classe, 10 second clips, single action per clip (kay, carreira, et al) kit robo-kitchen Activity data set - 540 clips of 17 people performing 12 complex kitchen activities.(L. Stiefelhagen) kth human action recognition database (kth cvap lab) Karlsruhe motion, Intention, and Activity data set (minta) - 7 types of activities of daily living including fully motion primitive segments.(D. Stiefelhagen) liris human Activities Dataset - contains (gray/rgb/depth) videos showing people performing various activities (Christian Wolf, et al, French National Center for Scientific Research) mexaction2 action detection and localization dataset - to support the development and evaluation of methods for 'spotting' instances of short actions. (Rohrbach, rohrbach, regneri, amin, Andriluka, pinkal, Schiele) msr-action3d - benchmark rgb-d action dataset (Microsoft Research Redmond and University of Wollongong) msractionPair dataset - : Histogram of Oriented 4d normals for Activity recognition from Depth Sequences (University of Central Florida and Microsoft) msrc-12 Kinect gesture data. (mivia lab at the University of Salemo) muhavi - multicamera human Action Video data (Hossein Ragheb) Multi-modal action detection (MAD) Dataset - it contains 35 sequential actions performed by 20 subjects. (Carnegiemellon University) Multiview 3d event dataset - this dataset includes 8 categories of events performed by 8 subjects (University of California at Los Angles) ntu rgbd action Recognition Dataset - ntu rgbd is a large scale dataset for human action recognition(Amir Shahroudy) Northwestern-ucla multiview Action. Kakadiaris) rgb-d activity dataset - each video in the dataset contains 2-7 actions involving interaction with different objects. (Cornell University and Stanford University) rgbd-action-Completion-2016 - this dataset includes 414 complete/incomplete object interaction sequences, spanning six actions and presenting rgb, depth and skeleton data. (Farnoosh heidarivincheh, majid Mirmehdi, dima damen) rgbd-sar dataset - rgbd-sar dataset (University of Electronic Science and Technology of China and Microsoft) Rochester Activities of daily living Dataset (Ross distributors Messing) sbu kinect Interaction Dataset - it contains eight types of interactions (Stony Brook university) sbu-kinect-Interaction dataset.
dataset of rgb-d video. Hospedales Edinburgh/qmul) behave interacting Person Video data with markup (Scott Blunsden, bob Fisher, Aroosha laghaee) bu-action Datasets - three image action datasets (BU101, bu101-unfiltered, bu203-unfiltered) that have 1:1 correspondence with classes of the video datasets ucf101 and ActivityNet. Sclaroff.) Berkeley mhad: a comprehensive multimodal Human Action Database (Ferda Ofli) Berkeley multimodal Human Action Database - five different modalities to expand the fields of application (University of California at Berkeley and Johns Hopkins University) Breakfast dataset - it's a dataset with 1712 video clips. Serre ) Bristol Egocentric Object Interactions Dataset - contains videos shot from a first-person (egocentric) point of view of 3-5 users performing tasks in six different locations (Dima damen, teesid leelaswassuk and Walterio mayol-cuevas, Bristol University) Brown Breakfast Actions Dataset - 70 hours, 4 million. (Sigurdsson, varol, wang, laptev, farhadi, gupta) Composable activities dataset - different combinations of 26 atomic actions formed 16 activity classes which were performed by 14 subjects and annotations were provided (Pontificia universidad Catolica de Chile and Universidad del Norte) Cornell Activity datasets cad 60, cad. The dataset is useful for highly structured behavior understanding (aizeboje, fisher) epic-kitchens - egocentric video recorded by 32 participants in their native kitchen environments, non-scripted daily activities,.5M frames,.6K frame-level action segments and 454.2K object bounding boxes (Damen, doughty, fidler, et al) epfl crepe. Gagnon) fcvid: Fudan-Columbia video dataset - 91,223 Web videos annotated manually according to 239 categories (Jiang, wu, wang, xue, chang) G3d - synchronised video, depth and skeleton data for 20 gaming actions captured with Microsoft Kinect (Victoria bloom) G3di - this dataset contains 12 subjects. (Bojanowski, lajugie, bach, laptev, ponce, schmid, and sivic) Humaneva : Synchronized Video and Motion Capture dataset for evaluation of Articulated Human Motion (Brown University) i-lids video event image dataset (Imagery library for intelligent detection systems) (Paul Hosner) I3DPost Multi-view Human Action Datasets (Hansung Kim) ias-lab. Papoutsakis) inria xmas Motion Acquisition Sequences (ixmas) (inria) Infar dataset -Infrared Action Recognition at Different Times neurocomputing(Chenqiang gao, yinhe du, jiang liu, jing lv, luyu yang, deyu meng, Alexander. Hauptmann) jhmdb: joints for the hmdb dataset (j-hmdb) based on 928 clips from hmdb51 comprising 21 action categories (Jhuang, gall, zuffi, schmid and Black) jpl first-Person Interaction dataset - 7 types of human activity videos taken from a first-person viewpoint (Michael.
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Hand, hand Grasp, hand Action and Gesture databases. Image, video and Shape database retrieval. Object Databases, people (static and dynamic human body pose. People detection and Tracking Databases (see also, surveillance remote sensing, scenes or Places, homme Scene segmentation or Classification. Segmentation, simultaneous Localization and Mapping, surveillance and Tracking (see also, people ). Textures, urban Datasets, vision and Natural Language, other Collection Pages. Miscellaneous Topics, another helpful site is the, yacvid page. See also: Action Recognition's dataset summary with league tables (Gall, kuehne, bhattarai).
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(Politecnico di torino/Computer Graphics vision Group) Spontaneous Emotion Multimodal Database (sem-db) - non-posed reactions to visual stimulus data recorded with hd rgb, depth and ir frames of the face, eeg signal and eye gaze data (Fernandez montenegro, gkelias, Argyriou) The unbc-mcMaster Shoulder pain Expression Archive. (Daniel McDuff.) Florence 2D/3d hybrid Face dataset - bridges the gap between 2d, appearance-based recognition techniques, and fully 3D approaches (Bagdanov, del Bimbo, and Masi) Facial Recognition Technology (feret) Database (usa national Institute of Standards and Technology) Gi4e database - eye-tracking database with. (Katrin Honauer, Ole johannsen, daniel Kondermann, bastian Goldluecke) a dataset for real Low-Light Image noise reduction - it contains pixel and intensity aligned pairs of images corrupted by low-light camera noise and their low-noise counterparts. ( bron ) Aambeien Aambeien ontstaan hoofdzakelijk als gevolg van constipatie. "At any rate, at the end of 1805 beethoven received a commission for these quartets from the count. . (Ali farhadi, ian Endres, derek hoiem, and david. (Farnoosh heidarivincheh, majid Mirmehdi, dima damen) rgbd-sar dataset - rgbd-sar dataset (University of Electronic Science and Technology of China and Microsoft) Rochester Activities of daily living Dataset (Ross Messing) sbu kinect Interaction Dataset - it contains eight types of interactions (Stony Brook university) sbu-kinect-Interaction dataset.
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Easy and safe parking, this is possible in the Interparking Centrum car park. Click here for more info. Pick-up service, pick up your online order at no charge in the Groningen branch. Call 31 (0) to make an appointment. Events, we are happy to let you become personally acquainted with our products. We regularly organize events in this branch. See the agenda here. Skins beauty bar, skins beauty bar is the treatment institute under lockwood the shop skins Cosmetics with two beauty salons, a pedicure station, a manicure salon and a hairdresser.
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CVonline: Image databases, this is a collated list of image and video swiss databases that people have found useful for computer vision research and algorithm evaluation. An important article, how good Is my test Data? Introducing Safety Analysis for Computer Vision (by zendel, murschitz, humenberger, and Herzner) introduces a methodology for ensuring that your dataset has sufficient variety that algorithm results on the dataset are representative of the results that one could expect in a real setting. In particular, the team have produced. Checklist of potential hazards (imaging situations) that may cause algorithms to have problems. Ideally, test datasets should have examples of the relevant hazards. Index by topic, action Databases, attribute recognition, autonomous Driving. Biological/Medical, camera calibration, face and eye/Iris Databases, fingerprints. General Images, general rgbd and depth datasets, general Videos.