still frame taken from indoor tracking results

Most algorithms for tracking objects in video consist of two components: a model of the dynamics of the object being tracked, and a model of its appearance. Often the appearance model is constructed before tracking, perhaps from training images, and then used as-is when tracking test sequences.

What if the test sequence contains appearances of the object, or lighting conditions, that don't exactly match those of the training data? Typically, trackers with fixed appearance models will perform poorly under these circumstances.

In this project we make use of the new appearance information that comes available during tracking to incrementally improve a subspace appearance model of the target. The key to this algorithm is a novel incremental algorithm for PCA, allowing for efficient subspace updates.



Code and Data

Matlab souce code for the algorithm can be obtained here.

The raw data sequences are also available. Feel free to use them to test your algorithms. The data used in the IJCV paper are available below as .mat files, for Matlab version 7 and above, and for Matlab 6. In two cases, the original color AVI videos are avaialble as well.

The raw videos used for the earlier papers are also available. All of the test videos are provided as as .pgm image files (one image per frame). If you don't see the sequence you're looking for, please send us an email.