Reliable Pedestrian Detection Using A Deep Neural Network Trained On Pedestrian Counts

Ghosh S, Amon P, Hutter A, Kaup A (2017)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2017

Pages Range: 685-689

Event location: Beijing CN

ISBN: 978-1-5090-2175-8

DOI: 10.1109/ICIP.2017.8296368

Abstract

Pedestrian detection is an important task for applications like surveillance, driver assistance systems and autonomous driving. We present a novel approach for detecting pedestrians using a deep convolutional neural network (CNN) trained for counting pedestrians. Our method avoids the need for annotation of the position of the pedestrians in the training data via bounding boxes. The deconvolved outputs of the filters of the trained counting model are used to detect the pedestrians. The average miss rate values on the tested datasets were found to be in the same range as other methods in spite of a simpler training using only pedestrian counts. This method is found to be suitable for detecting pedestrians in crowded scenes with occlusion as well as less crowded scenes.

Authors with CRIS profile

Sanjukta Ghosh Lehrstuhl für Multimediakommunikation und Signalverarbeitung (LMS) Andre Kaup Lehrstuhl für Multimediakommunikation und Signalverarbeitung (LMS)

How to cite

APA:

Ghosh, S., Amon, P., Hutter, A., & Kaup, A. (2017). Reliable Pedestrian Detection Using A Deep Neural Network Trained On Pedestrian Counts. In Proceedings of the IEEE International Conference on Image Processing (ICIP) (pp. 685-689). Beijing, CN.

MLA:

Ghosh, Sanjukta, et al. "Reliable Pedestrian Detection Using A Deep Neural Network Trained On Pedestrian Counts." Proceedings of the IEEE International Conference on Image Processing (ICIP), Beijing 2017. 685-689.

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