Details
| Original language | English |
|---|---|
| Title of host publication | Image Analysis |
| Subtitle of host publication | 18th Scandinavian Conference, SCIA 2013, Proceedings |
| Pages | 131-142 |
| Number of pages | 12 |
| Publication status | Published - 2013 |
| Event | 18th Scandinavian Conference on Image Analysis, SCIA 2013 - Espoo, Finland Duration: 17 Jun 2013 → 20 Jun 2013 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 7944 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
A Random Forest consists of several independent decision trees arranged in a forest. A majority vote over all trees leads to the final decision. In this paper we propose a Random Forest framework which incorporates a cascade structure consisting of several stages together with a bootstrap approach. By introducing the cascade, 99% of the test images can be rejected by the first and second stage with minimal computational effort leading to a massively speeded-up detection framework. Three different cascade voting strategies are implemented and evaluated. Additionally, the training and classification speed-up is analyzed. Several experiments on public available datasets for pedestrian detection, lateral car detection and unconstrained face detection demonstrate the benefit of our contribution.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
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Image Analysis: 18th Scandinavian Conference, SCIA 2013, Proceedings. 2013. p. 131-142 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7944 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Cascaded random forest for fast object detection
AU - Baumann, Florian
AU - Ehlers, Arne
AU - Vogt, Karsten
AU - Rosenhahn, Bodo
N1 - Funding information: This work has been partially funded by the ERC within the starting grant Dynamic MinVIP.
PY - 2013
Y1 - 2013
N2 - A Random Forest consists of several independent decision trees arranged in a forest. A majority vote over all trees leads to the final decision. In this paper we propose a Random Forest framework which incorporates a cascade structure consisting of several stages together with a bootstrap approach. By introducing the cascade, 99% of the test images can be rejected by the first and second stage with minimal computational effort leading to a massively speeded-up detection framework. Three different cascade voting strategies are implemented and evaluated. Additionally, the training and classification speed-up is analyzed. Several experiments on public available datasets for pedestrian detection, lateral car detection and unconstrained face detection demonstrate the benefit of our contribution.
AB - A Random Forest consists of several independent decision trees arranged in a forest. A majority vote over all trees leads to the final decision. In this paper we propose a Random Forest framework which incorporates a cascade structure consisting of several stages together with a bootstrap approach. By introducing the cascade, 99% of the test images can be rejected by the first and second stage with minimal computational effort leading to a massively speeded-up detection framework. Three different cascade voting strategies are implemented and evaluated. Additionally, the training and classification speed-up is analyzed. Several experiments on public available datasets for pedestrian detection, lateral car detection and unconstrained face detection demonstrate the benefit of our contribution.
UR - http://www.scopus.com/inward/record.url?scp=84884498174&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38886-6_13
DO - 10.1007/978-3-642-38886-6_13
M3 - Conference contribution
AN - SCOPUS:84884498174
SN - 9783642388859
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 131
EP - 142
BT - Image Analysis
T2 - 18th Scandinavian Conference on Image Analysis, SCIA 2013
Y2 - 17 June 2013 through 20 June 2013
ER -