ASTRE
A-contrario Smooth TRajectory Extraction

Presentation

Download / Installation and usage manual

Snow data sequence



Our work started with this simple observation: the human visual system is able to perceive motion hidden in high amounts of noise. How far could a computer go?

This is a 200 frames sequence of 180 noise points and one real trajectory. Can you spot it?

In the ASTRE framework, we derive from the a-contrario methodology a probability-based ``perceptual metric'' on the trajectories (combining their length, acceleration, number of holes, etc.). We also propose an efficient algorithm to extract the trajectories having the best appearance.

The principle of a-contrario algorithms is to control the number of false detections in the noise, and our method is thus resilient to high amounts of noise. The perceptual metric can also be used to filter the result of any other tracking algorithm, hence reducing the number of false detections.

Paper

M. Primet, L. Moisan, ``Point tracking: an a-contrario approach'', preprint MAP5, 2012. download: PDF

BibTeX Citation:

@unpublished{ASTRE2012,
  author = {M. Primet and L. Moisan},
  title={Point tracking: an a-contrario approach},
  note={Preprint MAP5},
  year={2012}
}

Snow sequence

We filmed a sequence of falling snow flakes in front of a tainted background, and extracted the motion of the snow flakes and of the background to obtain an interesting point sequence to compare point tracking algorithms.

Go to the snow data sequence page for a complete description and the sequence data files.

Snow sequence, original (subsampled) sequence.

Examples