ASTRE
Acontrario 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?
In the ASTRE framework, we derive from the acontrario methodology a probabilitybased ``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 acontrario 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 acontrario approach'', preprint MAP5, 2012. download:BibTeX Citation:
@unpublished{ASTRE2012, author = {M. Primet and L. Moisan}, title={Point tracking: an acontrario approach}, note={Preprint MAP5}, year={2012} }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.
Examples

Detections in high levels of noise
Our algorithm is able to cope with a high density of noise points. This example shows five trajectories hidden in 60 random noise points, that our algorithm is able to detect and extract.
Our algorithm makes very few false detections.

Robustness to model variations
Our algorithm is able to detect trajectories with high accelerations, and does not require a global parameter for all trajectories, but is able to adapt its detection thresholds to each trajectory in the data.

Robustness to parameter tuning
Only one parameter, easy to set (all detections shown on this page are obtained with the canonical maximal log(NFA) = 0 parameter). Changing the parameter has the effect of adding or removing trajectories, rather than changing completely their appearances.