Random Forests of Local Experts for Pedestrian Detection

Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the exist...

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Bibliographic Details
Main Authors: Marin, Javier, Vazquez, David, Lopez, Antonio M., Amores, Jaume, Leibe, Bastian
Format: Conference Proceeding
Language:English
Subjects:
Acceptability
Blocking
Computational efficiency
Computer vision
Conferences
Detectors
Feature extraction
Forests
HOG
LBP
Local Experts
Pedestrian detection
Pedestrians
Radio frequency
Random Forest
Support vector machines
Tasks
Training
Vectors
Vegetation
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Summary:Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In this paper, we propose a pedestrian detection method that efficiently combines multiple local experts by means of a Random Forest ensemble. The proposed method works with rich block-based representations such as HOG and LBP, in such a way that the same features are reused by the multiple local experts, so that no extra computational cost is needed with respect to a holistic method. Furthermore, we demonstrate how to integrate the proposed approach with a cascaded architecture in order to achieve not only high accuracy but also an acceptable efficiency. In particular, the resulting detector operates at five frames per second using a laptop machine. We tested the proposed method with well-known challenging datasets such as Caltech, ETH, Daimler, and INRIA. The method proposed in this work consistently ranks among the top performers in all the datasets, being either the best method or having a small difference with the best one.
ISSN:1550-5499
2380-7504
DOI:10.1109/ICCV.2013.322