17 April 2017

We are always looking for sharp and talented students who have a passion for machine vision and stochastic signal processing. If you are one of them, contact me and we may be able to provide you with a PhD scholarship.

1 March 2017

Check out our conference ICCAIS2017 that will be held in Thailand during Oct 31 - Nov 3, 2017. The papers are indexed by all major indexing databases such as Scopus and IEEExplore.

In a project funded by the ARC, I developed track-before detect methods for tracking an unknown number of objects in video, using the multi-Bernoulli filter. The major characteristic of my methods is that no explicit detection step is involved before tracking. Conventional methods involve using a feature analysis and detection algorithm to process each frame and result in a number of detections (also called point measurements in tracking literature), and those detections are used as measurements by a Baye's filter for tracking. In my method, the whole image is input by the filter as one measurement (a long vector or matrix of pixel values). Hence, the filter makes use of all the information available in the measurement for estimation and tracking purposes. Consequently, the estimation accuracy is improved.

Watch an example of multiple players being tracked in a soccer game. One version of my method accepts training samples (blobs containing the red shirt and shorts players, extracted from their previous games) and is trained using kernel density estimation.

Here is another example of tracking multiple live cells that are split and die in a micro-cell. The statistics of the tracker can be used by bio-medical researchers for cell lifetime analysis and early detection of cancer.

The multi-Bernoulli filter can be used with two update steps for sensor fusion. In the following example, the visual cues (camera image information) and audio cues (audio information from a stereo microphone) are fused to continuously track a speaker that temporarily hides from the camera.

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