Trajectory detection in the a-contrario framework

Table of contents

  1. Description
  2. Paper
  3. Download the code (see the manual)
  4. Snow data sequence
  5. Examples


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?

We propose the ASTRE [1] (A-contrario smooth trajectory extraction) framework based on the a-contrario methodology that defines a (quasi-parameterless) perceptual metric on the trajectories, and an efficient algorithm to extract the trajectory 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.


[1] M. Primet, L. Moisan, ``Point tracking: an a-contrario approach'', 2011.

BibTeX Citation:

  author = {M. Primet and L. Moisan},
  title={Point tracking: an a-contrario approach},

Download the code


This program is released under GPL License.

Feel free to use and adapt this code. However, if you use it for your research or in software code, please be so kind as to cite the paper [1].

Also, if you use, adapt or improve the code, we would love to hear about it!


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 cloud sequence to compare point tracking algorithms.

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

Snow sequence, original (subsampled) sequence.