Abstract
In this paper, we propose an ensemble classification approach to the Pedestrian Detection (PD) problem, resorting to distinct input channels and Convolutional Neural Networks (CNN). This methodology comprises two stages: (i) the proposals extraction, and (ii) the ensemble classification. In order to obtain the proposals, we apply several detectors specifically developed for the PD task. Afterwards, these proposals are converted into different input channels (e.g. gradient magnitude, LUV or RGB), and classified by each CNN. Finally, several ensemble methods are used to combine the output probabilities of each CNN model. By correctly selecting the best combination strategy, we achieve improvements, comparatively to the single CNN models predictions.
This work was partially supported by FCT[UID/EEA/50009/2013], and by the FCT project AHACMUP-ERI/HCI/0046/2013.
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Notes
- 1.
The six orientations are obtained in equally spaced intervals in the range \([0, \pi [\), see details in [15].
- 2.
Considering four input channel’s CNN models, the cardinality of G is \(|G|=15\).
- 3.
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Ribeiro, D., Carneiro, G., Nascimento, J.C., Bernardino, A. (2017). Multi-channel Convolutional Neural Network Ensemble for Pedestrian Detection. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_14
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