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Proceedings Paper

Remote pedestrians detection at night time in FIR Image using contrast filtering and locally projected region based CNN
Author(s): Taehwan Kim; Sungho Kim
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Paper Abstract

This paper presents a novel method to detect the remote pedestrians. After producing the human temperature based brightness enhancement image using the temperature data input, we generates the regions of interest (ROIs) by the multiscale contrast filtering based approach including the biased hysteresis threshold and clustering, remote pedestrian’s height, pixel area and central position information. Afterwards, we conduct local vertical and horizontal projection based ROI refinement and weak aspect ratio based ROI limitation to solve the problem of region expansion in the contrast filtering stage. Finally, we detect the remote pedestrians by validating the final ROIs using transfer learning with convolutional neural network (CNN) feature, following non-maximal suppression (NMS) with strong aspect ratio limitation to improve the detection performance. In the experimental results, we confirmed that the proposed contrast filtering and locally projected region based CNN (CFLP-CNN) outperforms the baseline method by 8% in term of logaveraged miss rate. Also, the proposed method is more effective than the baseline approach and the proposed method provides the better regions that are suitably adjusted to the shape and appearance of remote pedestrians, which makes it detect the pedestrian that didn’t find in the baseline approach and are able to help detect pedestrians by splitting the people group into a person.

Paper Details

Date Published: 16 May 2017
PDF: 16 pages
Proc. SPIE 10177, Infrared Technology and Applications XLIII, 101772G (16 May 2017); doi: 10.1117/12.2262270
Show Author Affiliations
Taehwan Kim, Yeungnam Univ. (Korea, Republic of)
Sungho Kim, Yeungnam Univ. (Korea, Republic of)

Published in SPIE Proceedings Vol. 10177:
Infrared Technology and Applications XLIII
Bjørn F. Andresen; Gabor F. Fulop; Charles M. Hanson; John Lester Miller; Paul R. Norton, Editor(s)

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