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Antoni B. Chan

Assistant Professor
Department of Computer Science
City University of Hong Kong
Hong Kong

Email: abchan at cityu dot edu dot hk

My webpage has moved to City University of Hong Kong, where I am an assistant professor in the Department of Computer Science. Below are links to some of my projects at SVCL. For a complete list of current projects, please see my website.

Main Research Topics:
Dynamic Textures
A family of generative stochastic dynamic texture models for analyzing motion.
Understanding Video of Crowded Environments
Motion segmentation and motion classification in video of crowded environments, such as pedestrian scenes and highway traffic.
Pedestrian Crowd Counting
We estimate the size of moving crowds in a privacy preserving manner, i.e. without people models or tracking. The system first segments the crowd by its motion, extracts low-level features from each segment, and estimates the crowd count in each segment using a Gaussian process.
[project | demo | PETS2009 demo]
Layered Dynamic Textures
One disadvantage of the dynamic texture is its inability to account for multiple co-occuring textures in a single video. We extend the dynamic texture to a multi-state (layered) dynamic texture that can learn regions containing different dynamic textures.
Kernel Dynamic Textures
We introduce a kernelized dynamic texture, which has a non-linear observation function learned with kernel PCA. The new texture model can account for more complex patterns of motion, such as chaotic motion (e.g. boiling water and fire) and camera motion (e.g. panning and zooming), better than the original dynamic texture.
[demo coming soon]
Classification and Retrieval of Traffic Video
We classify traffic congestion in video by representing the video as a dynamic texture, and classifying it using an SVM with a probabilistic kernel (the KL kernel). The resulting classifier is robust to noise and lighting changes.
[project | demo]
Modeling video with Mixtures of Dynamic Textures
We introduce the mixture of dynamic textures, which models a collection of video as samples from a set of dynamic textures. We use the model for video clustering and motion segmentation.
[project | demo]
Semantic Image Annotation and Retrieval
We annotate images using supervised multi-class labeling (SML), which treats semantic annotation as a multi-class classification problem. The system is scalable, and was applied to image databases with 60,000 images.
[project | annotation and retrieval demos]
[SML for audio annotation and retrieval]
Music Segmentation
We model a time-series of audio feature vectors, extracted from a short audio fragment, as a dynamic texture. The musical structure of a song (e.g. chorus, verse, and bridge) is discovered by segmenting the song using the mixture of dynamic textures. The song segmentations are used for song retrieval, song annotation, and database visualization.

Last update: 08/27/2009


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