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Sensors 2018, 18(7), 2272; https://doi.org/10.3390/s18072272

An Occlusion-Robust Feature Selection Framework in Pedestrian Detection

1
Department of Telecommunications and Information Processing, Ghent University-Interuniversitair Micro-Elektronica Centrum (IMEC), Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium
2
School of Information Science and Engineering, Shandong University, Jinan 250100, China
This paper is an extended version of our paper published in Guo, Z.; Liao, W.; Veelaert, P.; Philips, W. Occlusion-Robust Detector Trained with Occluded Pedestrians. In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods (ICPRAM2018), Funchal, Portugal, 16–18 January 2018.
*
Author to whom correspondence should be addressed.
Received: 7 June 2018 / Revised: 9 July 2018 / Accepted: 10 July 2018 / Published: 13 July 2018
(This article belongs to the Section Intelligent Sensors)
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Abstract

Better features have been driving the progress of pedestrian detection over the past years. However, as features become richer and higher dimensional, noise and redundancy in the feature sets become bigger problems. These problems slow down learning and can even reduce the performance of the learned model. Current solutions typically exploit dimension reduction techniques. In this paper, we propose a simple but effective feature selection framework for pedestrian detection. Moreover, we introduce occluded pedestrian samples into the training process and combine it with a new feature selection criterion, which enables improved performances for occlusion handling problems. Experimental results on the Caltech Pedestrian dataset demonstrate the efficiency of our method over the state-of-art methods, especially for the occluded pedestrians. View Full-Text
Keywords: pedestrian detection; feature selection; occlusion handling; deep learning pedestrian detection; feature selection; occlusion handling; deep learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Guo, Z.; Liao, W.; Xiao, Y.; Veelaert, P.; Philips, W. An Occlusion-Robust Feature Selection Framework in Pedestrian Detection . Sensors 2018, 18, 2272.

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