Point tracking: an a-contrario approach
type: Dataset
description: In this work, we propose a new approach to recover trajectories from points previously detected in a sequence of images. In presence of spurious and missing detections, actual trajectories can be characterized by an a-contrario model, that estimates the probability of observing a similar trajectory in random data. This results in a single criterion combining trajectory characteristics (duration, number of points, smoothness) and data statistics (number of images and detected points), which can then be used to drive a dynamic programming algorithm able to extract sequentially the most meaningful trajectories. The performances obtained on synthetic and real-world data are studied in detail, and shown to compare favorably to the state-of-the-art ROADS algorithm.
issued: 2026-05-26T10:53:59.645914
language: en
modified: 2026-05-26T10:53:59.645924
contact point: Organization
  fn: CCSD
keyword: a-contrario-detection
keyword: infoeu-reposemanticspreprint
keyword: infoinfo-ticomputer-science-csimage-processing-eessiv
keyword: motion-correspondence
keyword: point-tracking
keyword: preprints-working-papers-
landing page: https://hal.science/hal-00675083

Point tracking: an a-contrario approach
type: Distribution
format: HTML
issued: 2026-05-26T10:53:59.703198
modified: 2026-05-26T10:53:59.603867
access URL: https://hal.science/hal-00675083

test_moissonnage_selune
type: Agent