reformating README for better referencing

Author: jchiquet

README.md

@@ -1,6 +1,11 @@
 # Macrolitter video counting on riverbanks using state space models and moving cameras
 
 [![build and publish](https://github.com/computorg/published-202301-chagneux-macrolitter/actions/workflows/build.yml/badge.svg)](https://github.com/computorg/published-202301-chagneux-macrolitter/actions/workflows/build.yml)
+[![DOI](https://img.shields.io/badge/DOI-10.57750%2F845m--f805-034E79.svg)](https://doi.org/10.57750/845m-f805)
+[![reviews](https://img.shields.io/badge/review-report-blue)](https://github.com/computorg/published-202301-chagneux-macrolitter/issues?q=is%3Aopen+is%3Aissue+label%3Areview)
+[![SWH](https://archive.softwareheritage.org/badge/origin/https://github.com/computorg/published-202301-chagneux-macrolitter/)](https://archive.softwareheritage.org/browse/origin/?origin_url=https://github.com/computorg/published-202301-chagneux-macrolitter)
+[![Creative Commons License](https://i.creativecommons.org/l/by/4.0/80x15.png)](http://creativecommons.org/licenses/by/4.0/)
+ISSN 2824-7795
 
 Authors:
 
@@ -11,10 +16,5 @@ Authors:
 - Océane Lepâtre, Surfrider Foundation Europe
 - Antoine Bruge, Surfrider Foundation Europe
 
-[![DOI](https://img.shields.io/badge/DOI-10.57750%2F845m--f805-034E79.svg)](https://doi.org/10.57750/845m-f805)
-[![HTML](https://img.shields.io/badge/article-HTML-034E79)](https://computorg.github.io/published-202301-chagneux-macrolitter/)
-[![review 1](https://img.shields.io/badge/review-report%201-blue)](https://github.com/computorg/published-202301-chagneux-macrolitter/issues/1)
-[![review 2](https://img.shields.io/badge/review-report%202-blue)](https://github.com/computorg/published-202301-chagneux-macrolitter/issues/2)
-[![SWH](https://archive.softwareheritage.org/badge/origin/https://github.com/computorg/published-202301-chagneux-macrolitter/)](https://archive.softwareheritage.org/browse/origin/?origin_url=https://github.com/computorg/published-202301-chagneux-macrolitter)
-[![Creative Commons License](https://i.creativecommons.org/l/by/4.0/80x15.png)](http://creativecommons.org/licenses/by/4.0/)
-ISSN 2824-7795
+Litter is a known cause of degradation in marine environments and most of it travels in rivers before reaching the oceans. In this paper, we present a novel algorithm to assist waste monitoring along watercourses. While several attempts have been made to quantify litter using neural object detection in photographs of floating items, we tackle the more challenging task of counting directly in videos using boat-embedded cameras. We rely
+on multi-object tracking (MOT) but focus on the key pitfalls of false and redundant counts which arise in typical scenarios of poor detection performance. Our system only requires supervision at the image level and performs Bayesian filtering via a state space model based on optical flow. We present a new open image dataset gathered through a crowdsourced campaign and used to train a center-based anchor-free object detector. Realistic video footage assembled by water monitoring experts is annotated and provided for evaluation. Improvements in count quality are demonstrated against systems built from state-of-the-art multi-object trackers sharing the same detection capabilities. A precise error decomposition allows clear analysis and highlights the remaining challenges.

_quarto.yml

@@ -1,5 +1,80 @@
 project:
-  title: "chagneux-macrolitter"
-
+  render:
+    - published-202301-chagneux-macrolitter.qmd
 
+title: "Macrolitter video counting on riverbanks using state space models and moving cameras "
+subtitle: ""
+author:
+  - name: "Mathis Chagneux"
+    corresponding: true
+    email: mathis.chagneux@telecom-paris.fr
+    url: https://www.linkedin.com/in/mathis-chagneux-140245158/?originalSubdomain=fr
+    affiliation: Telecom Paris, LTCI
+    affiliation-url: https://www.telecom-paris.fr/fr/recherche/laboratoires/laboratoire-traitement-et-communication-de-linformation-ltci
+  - name: "Sylvain Le Corff"
+    email: sylvain.le_corff@sorbonne-universite.fr
+    url: https://sylvainlc.github.io/
+    orcid: 0000-0001-5211-2328
+    affiliation: Sorbonne Université, UMR 8001 (LPSM)
+    affiliation-url: https://www.lpsm.paris/
+  - name: "Pierre Gloaguen"
+    email: pierre.gloaguen@agroparistech.fr
+    url: https://papayoun.github.io/
+    orcid: 0000-0003-2239-5413
+    affiliation: AgroParisTech, UMR MIA 518
+    affiliation-url: https://mia-ps.inrae.fr/
+  - name: "Charles Ollion"
+    email: charles.ollion@gmail.com
+    url: https://charlesollion.github.io/
+    orcid: 0000-0002-6763-701X
+    affiliation: Naia Science
+  - name: "Océane Lepâtre"
+    email: olepatre@surfrider.eu
+    url: https://fr.linkedin.com/in/oc%C3%A9ane-lep%C3%A2tre-675b38116
+    orcid: None
+    affiliation: Surfrider Foundation Europe
+    affiliation-url: https://surfrider.eu/
+  - name: "Antoine Bruge"
+    email: antoine.bruge@outlook.com
+    url: https://www.linkedin.com/in/antoinebruge/
+    orcid: 0000-0002-0548-234X
+    affiliation: Surfrider Foundation Europe
+    affiliation-url: https://surfrider.eu/ 
+date: 2023-02-16
+date-modified: last-modified
+abstract: >+
+    Litter is a known cause of degradation in marine environments and most of
+    it travels in rivers before reaching the oceans. In this paper, we present
+    a novel algorithm to assist waste monitoring along watercourses. While
+    several attempts have been made to quantify litter using neural object
+    detection in photographs of floating items, we tackle the more challenging
+    task of counting directly in videos using boat-embedded cameras. We rely
+    on multi-object tracking (MOT) but focus on the key pitfalls of false and
+    redundant counts which arise in typical scenarios of poor detection
+    performance. Our system only requires supervision at the image level and
+    performs Bayesian filtering via a state space model based on optical flow.
+    We present a new open image dataset gathered through a crowdsourced
+    campaign and used to train a center-based anchor-free object detector.
+    Realistic video footage assembled by water monitoring experts is annotated
+    and provided for evaluation. Improvements in count quality are
+    demonstrated against systems built from state-of-the-art multi-object
+    trackers sharing the same detection capabilities. A precise error
+    decomposition allows clear analysis and highlights the remaining
+    challenges.
+citation:
+  type: article-journal
+  container-title: "Computo"
+  doi: "10.57750/845m-f805"
+  publisher: "French Statistical Society"
+  issn: "2824-7795"
+google-scholar: true
+bibliography: references.bib
+github-user: computorg
+repo: "published-202301-chagneux-macrolitter"
+draft: false
+published: true
+format:
+  computo-html: default
+  computo-pdf: default
+jupyter: python3
 

published-202301-chagneux-macrolitter.qmd

@@ -1,80 +1,3 @@
----
-title: "Macrolitter video counting on riverbanks using state space models and moving cameras "
-subtitle: ""
-author:
-  - name: "Mathis Chagneux"
-    corresponding: true
-    email: mathis.chagneux@telecom-paris.fr
-    url: https://www.linkedin.com/in/mathis-chagneux-140245158/?originalSubdomain=fr
-    affiliation: Telecom Paris, LTCI
-    affiliation-url: https://www.telecom-paris.fr/fr/recherche/laboratoires/laboratoire-traitement-et-communication-de-linformation-ltci
-  - name: "Sylvain Le Corff"
-    email: sylvain.le_corff@sorbonne-universite.fr
-    url: https://sylvainlc.github.io/
-    orcid: 0000-0001-5211-2328
-    affiliation: Sorbonne Université, UMR 8001 (LPSM)
-    affiliation-url: https://www.lpsm.paris/
-  - name: "Pierre Gloaguen"
-    email: pierre.gloaguen@agroparistech.fr
-    url: https://papayoun.github.io/
-    orcid: 0000-0003-2239-5413
-    affiliation: AgroParisTech, UMR MIA 518
-    affiliation-url: https://mia-ps.inrae.fr/
-  - name: "Charles Ollion"
-    email: charles.ollion@gmail.com
-    url: https://charlesollion.github.io/
-    orcid: 0000-0002-6763-701X
-    affiliation: Naia Science
-  - name: "Océane Lepâtre"
-    email: olepatre@surfrider.eu
-    url: https://fr.linkedin.com/in/oc%C3%A9ane-lep%C3%A2tre-675b38116
-    orcid: None
-    affiliation: Surfrider Foundation Europe
-    affiliation-url: https://surfrider.eu/
-  - name: "Antoine Bruge"
-    email: antoine.bruge@outlook.com
-    url: https://www.linkedin.com/in/antoinebruge/
-    orcid: 0000-0002-0548-234X
-    affiliation: Surfrider Foundation Europe
-    affiliation-url: https://surfrider.eu/ 
-date: 2023-02-16
-date-modified: last-modified
-abstract: >+
-    Litter is a known cause of degradation in marine environments and most of
-    it travels in rivers before reaching the oceans. In this paper, we present
-    a novel algorithm to assist waste monitoring along watercourses. While
-    several attempts have been made to quantify litter using neural object
-    detection in photographs of floating items, we tackle the more challenging
-    task of counting directly in videos using boat-embedded cameras. We rely
-    on multi-object tracking (MOT) but focus on the key pitfalls of false and
-    redundant counts which arise in typical scenarios of poor detection
-    performance. Our system only requires supervision at the image level and
-    performs Bayesian filtering via a state space model based on optical flow.
-    We present a new open image dataset gathered through a crowdsourced
-    campaign and used to train a center-based anchor-free object detector.
-    Realistic video footage assembled by water monitoring experts is annotated
-    and provided for evaluation. Improvements in count quality are
-    demonstrated against systems built from state-of-the-art multi-object
-    trackers sharing the same detection capabilities. A precise error
-    decomposition allows clear analysis and highlights the remaining
-    challenges.
-citation:
-  type: article-journal
-  container-title: "Computo"
-  doi: "10.57750/845m-f805"
-  publisher: "French Statistical Society"
-  issn: "2824-7795"
-google-scholar: true
-bibliography: references.bib
-github-user: computorg
-repo: "published-202301-chagneux-macrolitter"
-draft: false
-published: true
-format:
-  computo-html: default
-  computo-pdf: default
-jupyter: python3
----
 
 # Introduction