2019
Egocentric hand detection dataset with variability on people, activities and places, to simulate daily life situations. Datasets for multi-view crowd counting in wide-area scenes. Includes our CityStreet dataset, as well as the counting and metadata for multi-view counting on PETS2009 and DukeMTMC. Crowd counting dataset of indoor/outdoor scenes with extrinsic camera parameters (camera angle and height), for use as side information. Toolboxes for density-preserving HEM algorithm for simplifying mixture models.EgoDaily – Egocentric dataset for Hand Disambiguation
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Image and Vision Computing, 89:131-143, Sept 2019. CityStreet: Multi-view crowd counting dataset
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In: IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), Long Beach, June 2019. CityUHK-X: crowd dataset with extrinsic camera parameters
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In: Neural Information Processing Systems, Long Beach, Dec 2017. DPHEM toolbox for simplifying GMMs
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 41(6):1323-1337, June 2019.
2018
2017
A multi-view and stereo-depth dataset for 3D human pose estimation, which consists of challenging martial arts actions (Tai-chi and Karate), dancing actions (hip-hop and jazz), and sports actions (basketball, volleyball, football, rugby, tennis and badminton). This is a MATLAB toolbox for analyzing eye movement data using hidden Markov models. It includes code for learning HMMs for individuals, as well as clustering indivduals’ HMMs into groups.MADS: Martial Arts, Dancing, and Sports Dataset
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Image and Vision Computing, 61:22-39, May 2017. Eye Movement Hidden Markov Models (EMHMM) Toolbox
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Journal of Vision, 14(11):8, Sep 2014.
2016
This is a MATLAB toolbox for automatically extracting the panels from digital manga/comic pages. The VarBB toolbox is an implementation for the variational branch-and-bound algorithm for Bregman ball trees (bb-trees). VarBB can speed up nearest neighbor search for generative models. This is a MATLAB toolbox for clustering hidden Markov models using the variational HEM algorithm. The toolbox can also estimate HMM mixtures (H3M) using the EM algorithm. Images of small objects for small instance detections. Currently four object types are available.Manga panel extraction toolbox
VarBB Toolbox
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In: International Conference on Machine Learning (ICML), Atlanta, Jun 2013. H3M toolbox
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Journal of Machine Learning Research (JMLR), 15(2):697-747, Feb 2014. Small Object Dataset
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In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Boston, Jun 2015.
2015
2014
This is an OpenCV C++ library for Dynamic Teture (DT) models. It contains code for the EM algorithm for learning DTs and DT mixture models, and the HEM algorithm for clustering DTs, as well as DT-based applications, such as motion segmentation and Bag-of-Systems (BoS) motion descriptors. This is a toolbox for generalized Gaussian process models (GGPM). The toolbox is implemented as an add-on to the GPML toolbox for Matlab/Octave. The toolbox contains likelihood functions for GGPMs, as well as a Taylor inference function. GPML version 3.4 is supported.libdt – OpenCV library for Dynamic Textures
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 30(5):909-926, May 2008.
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 35(7):1606-1621, Jul 2013. Generalized Gaussian Process Models Toolbox
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In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Colorado Springs, Jun 2011.
2013
Videos can be obtained from the DynTex website. The text files contain the list of selected tags, the list of selected videos and ground-truth tags, and the training/test set splits. Video of people on pedestrian walkways at UCSD, and the corresponding motion segmentations. Currently two scenes are available. People annotations, perspective density maps, region-of-interest, and crowd counts for the UCSD Pedestrian Dataset. The features and counts for people counting on the UCSD Dataset. This data should be sufficient if you are interested in the regression problem only. Includes the Peds1, Peds2, and CVPR counting datasets. The features and counts for people counting on the PETS2009 Dataset. Also includes the segmentations, perspective maps, and ground-truth annotations. These are the ground-truth annotations for line counting on the UCSD, Grand Central, and LHI datasets.Experimental setup for semantic video texture annotation on the DynTex dataset
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 35(7):1606-1621, Jul 2013. UCSD Pedestrian Dataset
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IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 30(5):909-926, May 2008. People Annotations for UCSD Dataset
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IEEE Trans. on Image Processing (TIP), 21(4):2170-2177, May 2012. People Counting Data for UCSD Dataset
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IEEE Trans. on Image Processing (TIP), 21(4):2170-2177, May 2012. People Counting Data for PETS2009 Dataset
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In: 11th IEEE Intl. Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2009), Miami, Jun 2009. Line Counting Dataset
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IEEE Trans. on Circuits and Systems for Video Technology (TCSVT), 26(10):1955-1969, Oct 2016.
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In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Portland, Jun 2013.
2012
Dataset of manga panel layouts.Manga Layout Dataset
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ACM Transactions on Graphics (Proc. SIGGRAPH Asia 2012), Singapore, Nov 2012.
2011
A video of boats moving through water. A challenging background subtraction task, where the background itself is moving. Ground-truth annotations of the musical keys of songs in the GTZAN music genre dataset.Boats Videos
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Machine Vision and Applications, 22(5):751-766, Sep 2011. Key annotations for the GTZAN music genre dataset
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In: Intl. Conference on MultiMedia Modeling (MMM), Taipei, Jan 2011.