Downloads (by year)

This is the toolbox for modeling eye movements and feature learning with deep neural networks and hidden Markov models (DNN+HMM).

• Files: download here
• Project page
• If you use this toolbox please cite:
  
  Understanding the role of eye movement consistency in face recognition and autism through integrating deep neural networks and hidden Markov models.
  Janet H. Hsiao, Jeehye An, Veronica Kit Sum Hui, Yueyuan Zheng, and Antoni B. Chan,
  npj Science of Learning, 7:28, Oct 2022.

A dataset consisting of  Chinese White Dolphin (CWD) and distractors for detection tasks.

• Files: Google Drive, Readme
  
• Project page
• If you use this dataset please cite:
  
  Chinese White Dolphin Detection in the Wild.
  Hao Zhang, Qi Zhang, Phuong Anh Nguyen, Victor Lee, and Antoni B. Chan,
  In: ACM Multimedia Asia (MMAsia), Gold Coast, Australia, Dec 2021.

Generalized loss function for crowd counting.

• Files: github
• Project page
• If you use this toolbox please cite:
  
  Dynamic Momentum Adaptation for Zero-Shot Cross-Domain Crowd Counting.
  Qiangqiang Wu, Jia Wan, and Antoni B. Chan,
  In: ACM Multimedia (MM), Oct 2021.

Synthetic dataset for cross-view cross-scene multi-view counting. The dataset contains 31 scenes, each with about ~100 camera views. For each scene, we capture 100 multi-view images of crowds.

• Files: Google Drive
  
• Project page
• If you use this dataset please cite:
  
  Cross-View Cross-Scene Multi-View Crowd Counting.
  Qi Zhang, Wei Lin, and Antoni B. Chan,
  In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR):557-567, Jun 2021.

Generalized loss function for crowd counting.

• Files: github
• Project page
• If you use this toolbox please cite:
  
  A Generalized Loss Function for Crowd Counting and Localization.
  Jia Wan, Ziquan Liu, and Antoni B. Chan,
  In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2021.

Dataset for fine-grained crowd counting, which differentiates a crowd into categories based on the low-level behavior attributes of the individuals (e.g. standing/sitting or violent behavior) and then counts the number of people in each category.

• Files: dataset (1.2GB), code
• Project Page
• If you use this dataset please cite:
  
  Fine-Grained Crowd Counting.
  Jia Wan, Nikil S. Kumar, and Antoni B. Chan,
  IEEE Trans. on Image Processing (TIP), 30:2114-2126, Jan 2021.

This is a python toolbox learning parametric manifolds of Gaussian mixture models (GMMs).

• Files: download here
• Project page
• If you use this toolbox please cite:
  
  PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture Models.
  Ziquan Liu, Lei Yu, Janet H. Hsiao, and Antoni B. Chan,
  IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 44(6):3197-3211, June 2022 (online 2021).

Modeling noisy annotations in crowd counting: NoisyCC.

• Files: github
• Project page
• If you use this toolbox please cite:
  
  Modeling Noisy Annotations for Crowd Counting.
  Jia Wan and Antoni B. Chan,
  In: Neural Information Processing Systems (NeurIPS), Dec 2020.

Recurrently optimized tracking with ROAM and ROAM++.

• Files: github
• Project page
• If you use this toolbox please cite:
  
  ROAM: Recurrently Optimizing Tracking Model.
  Tianyu Yang, Pengfei Xu, Runbo Hu, Hua Chai, and Antoni B. Chan,
  In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Jun 2020.

KDMG for crowd counting.

• Files: github
• Project page
• If you use this toolbox please cite:
  
  Kernel-based Density Map Generation for Dense Object Counting.
  Jia Wan, Qingzhong Wang, and Antoni B. Chan,
  IEEE Trans. Pattern Analysis and Machine Intelligence (TPAMI), 44(3):1357-1370, Mar 2022.

This is a MATLAB toolbox for analyzing eye movement data using switching hidden Markov models (SHMMs), for analyzing eye movement data in cognitive tasks involving cognitive state changes. It includes code for learning SHMMs for individuals, as well as analyzing the results.

• Files: download here
• Project page
• If you use this toolbox please cite:
  
  Eye movement analysis with switching hidden Markov models.
  Tim Chuk, Antoni B. Chan, Shinsuke Shimojo, and Janet H. Hsiao,
  Behavior Research Methods, 52:1026-1043, June 2020.

Egocentric hand detection dataset with variability on people, activities and places, to simulate daily life situations.

• Files: download page
• If you use this dataset please cite:
  
  Is that my hand? An egocentric dataset for hand disambiguation.
  Sergio R. Cruz and Antoni B. Chan,
  Image and Vision Computing, 89:131-143, Sept 2019.

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.

• Files: download page
• Project page
• If you use this dataset please cite:
  
  Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs.
  Qi Zhang and Antoni B. Chan,
  In: IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), Long Beach, June 2019.

The code/model for wide-area crowd counting using multiple views: multi-view multi-scale (MVMS) model.

• Files: github
• Project page
• If you use this toolbox please cite:
  
  Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs.
  Qi Zhang and Antoni B. Chan,
  In: IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), Long Beach, June 2019.

Toolbox for computing diversity metrics for image captioning.

• Files: github
• Project page
• If you use this toolbox please cite:
  
  On Diversity in Image Captioning: Metrics and Methods.
  Qingzhong Wang, Jia Wan, and Antoni B. Chan,
  IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 44(2):1035-1049, Feb 2022.

The code/model for crowd counting using residual regression and semantic prior.

• Files: github
• Project page
• If you use this toolbox please cite:
  
  Residual Regression with Semantic Prior for Crowd Counting.
  Jia Wan, Wenhan Luo, Baoyuan Wu, Antoni B. Chan, and Wei Liu,
  In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Long Beach, June 2019.

Crowd counting dataset of indoor/outdoor scenes with extrinsic camera parameters (camera angle and height), for use as side information.

• Files: zip (1.8GB) | readme
• Project page
• If you use this dataset please cite:
  
  Incorporating Side Information by Adaptive Convolution.
  Di Kang, Debarun Dhar, and Antoni B. Chan,
  In: Neural Information Processing Systems, Long Beach, Dec 2017.

Toolboxes for density-preserving HEM algorithm for simplifying mixture models.

• Files: Python toolbox (compatible with sklearn.mixture.GaussianMixture) and Matlab toolbox
• If you use this code please cite:
  
  Density-Preserving Hierarchical EM Algorithm: Simplifying Gaussian Mixture Models for Approximate Inference.
  Lei Yu, Tianyu Yang, and Antoni B. Chan,
  IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 41(6):1323-1337, June 2019.

The code/model for GHA for image captioning.

• Files: github
• Project page
• If you use this toolbox please cite:
  
  Gated Hierarchical Attention for Image Captioning.
  Qingzhong Wang and Antoni B. Chan,
  In: Asian Conference on Computer Vision (ACCV), Perth, Dec 2018.

The code/models for MemTrack and MemDTC for visual object tracking

• Files: MemTrack (ECCV), MemDTC (TPAMI)
• Project page
• If you use this toolbox please cite:
  
  Visual Tracking via Dynamic Memory Networks.
  Tianyu Yang and Antoni B. Chan,
  IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 43(1):360-374, Jan 2021.

This is a code/model for Recurrent Filter Learning for VOT.

• Files: GitHub
• Project page
• If you use this toolbox please cite:
  
  Recurrent filter learning for visual tracking.
  Tianyu Yang and Antoni B. Chan,
  In: ICCV 5th Visual Object Tracking Challenge Workshop VOT2017, Venice, Oct 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).

• Files: download here
• Project page
• If you use this dataset please cite:
  
  Martial Arts, Dancing and Sports Dataset: a Challenging Stereo and Multi-View Dataset for 3D Human Pose Estimation.
  Weichen Zhang, Zhiguang Liu, Liuyang Zhou, Howard Leung, and Antoni B. Chan,
  Image and Vision Computing, 61:22-39, May 2017.

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.

• Files: download here
• Project page
• If you use this toolbox please cite:
  
  Understanding eye movements in face recognition using hidden Markov models.
  Tim Chuk, Antoni B. Chan, and Janet H. Hsiao,
  Journal of Vision, 14(11):8, Sep 2014.

This is a MATLAB toolbox for automatically extracting the panels from digital manga/comic pages.

• Files: zip
• If you use this code please cite:
  
  A Robust Panel Extraction Method for Manga.
  Xufang Pang, Ying Cao, Rynson W.H. Lau, and Antoni B. Chan,
  In: ACM Multimedia, Orlando, Nov 2014.

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.

• Files: zip
• If you use this code please cite:
  
  That was fast! Speeding up NN search of high dimensional distributions.
  Emanuele Coviello, Adeel Mumtaz, Antoni B. Chan, and Gert R.G. Lanckriet,
  In: International Conference on Machine Learning (ICML), Atlanta, Jun 2013.

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.

• Files: zip
• If you use this code please cite:
  
  Clustering hidden Markov models with variational HEM.
  Emanuele Coviello, Antoni B. Chan, and Gert R.G. Lanckriet,
  Journal of Machine Learning Research (JMLR), 15(2):697-747, Feb 2014.

Images of small objects for small instance detections.  Currently four object types are available.

• Files: zip (5.9 MB)
• If you use this dataset please cite:
  
  Small Instance Detection by Integer Programming on Object Density Maps.
  Zheng Ma, Lei Yu, and Antoni B. Chan,
  In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Boston, Jun 2015.

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.

• Files: zip (v1.01) | readme
• If you use this code please cite:
  
  • Modeling, clustering, and segmenting video with mixtures of dynamic textures.
    Antoni B. Chan and Nuno Vasconcelos,
    IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 30(5):909-926, May 2008.
  • Clustering Dynamic Textures with the Hierarchical EM Algorithm for Modeling Video.
    Adeel Mumtaz, Emanuele Coviello, Gert R.G. Lanckriet, and Antoni B. Chan,
    IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 35(7):1606-1621, Jul 2013.

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.

• Files: zip | readme
• If you use this code please cite:
  
  Generalized Gaussian Process Models.
  Antoni B. Chan and Daxiang Dong,
  In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Colorado Springs, Jun 2011.

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.

• Files: ground-truth | setup
• If you use this dataset please cite:
  
  Clustering Dynamic Textures with the Hierarchical EM Algorithm for Modeling Video.
  Adeel Mumtaz, Emanuele Coviello, Gert R.G. Lanckriet, and Antoni B. Chan,
  IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 35(7):1606-1621, Jul 2013.

Video of people on pedestrian walkways at UCSD, and the corresponding motion segmentations. Currently two scenes are available.

• Files: vidf (787MB) | vidd (672MB) | readme
• If you use this dataset please cite:
  
  Modeling, clustering, and segmenting video with mixtures of dynamic textures.
  Antoni B. Chan and Nuno Vasconcelos,
  IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 30(5):909-926, May 2008.

People annotations, perspective density maps, region-of-interest, and crowd counts for the UCSD Pedestrian Dataset.

• Files: zip (5.3MB) | readme
• If you use this dataset please cite:
  
  Counting People with Low-Level Features and Bayesian Regression.
  Antoni B. Chan and Nuno Vasconcelos,
  IEEE Trans. on Image Processing (TIP), 21(4):2170-2177, May 2012.

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.

• Files: zip (7MB) | readme
• If you use this dataset please cite:
  
  Counting People with Low-Level Features and Bayesian Regression.
  Antoni B. Chan and Nuno Vasconcelos,
  IEEE Trans. on Image Processing (TIP), 21(4):2170-2177, May 2012.

The features and counts for people counting on the PETS2009 Dataset.  Also includes the segmentations, perspective maps, and ground-truth annotations.

• Files: zip (4.2MB) | readme
• If you use this dataset please cite:
  
  Analysis of Crowded Scenes using Holistic Properties.
  Antoni B. Chan, Mulloy Morrow, and Nuno Vasconcelos,
  In: 11th IEEE Intl. Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2009), Miami, Jun 2009.

These are the ground-truth annotations for line counting on the UCSD, Grand Central, and LHI datasets.

• Files: zip (7 MB)
• If you use this dataset please cite:
  
  Counting People Crossing a Line using Integer Programming and Local Features.
  Zheng Ma and Antoni B. Chan,
  IEEE Trans. on Circuits and Systems for Video Technology (TCSVT), 26(10):1955-1969, Oct 2016.
  Crossing the Line: Crowd Counting by Integer Programming with Local Features.
  Zheng Ma and Antoni B. Chan,
  In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Portland, Jun 2013.

Dataset of manga panel layouts.

• Files: zip
• If you use this dataset please cite:
  
  Automatic Stylistic Manga Layout.
  Ying Cao, Antoni B. Chan, and Rynson W.H. Lau,
  ACM Transactions on Graphics (Proc. SIGGRAPH Asia 2012), Singapore, Nov 2012.

A video of boats moving through water.  A challenging background subtraction task, where the background itself is moving.

• Files: zip (135MB) | readme
• If you use this dataset please cite:
  
  Generalized Stauffer-Grimson background subtraction for dynamic scenes.
  Antoni B. Chan, Vijay Mahadevan, and Nuno Vasconcelos,
  Machine Vision and Applications, 22(5):751-766, Sep 2011.

Ground-truth annotations of the musical keys of songs in the GTZAN music genre dataset.

• Files: zip
• If you use this dataset please cite:
  
  Genre Classification and the Invariance of MFCC Features to Key and Tempo.
  Tom L.H. Li and Antoni B. Chan,
  In: Intl. Conference on MultiMedia Modeling (MMM), Taipei, Jan 2011.