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Pedestrian detection using first- and second-order aggregate channel features

Pedestrian detection using first- and second-order aggregate channel features The content-based analysis of visual multimedia like images and videos are urgently needed to empower human society for the automation of difficult tasks. Pedestrian detection serves as a backbone for a multitude of image processing and machine learning algorithms and secures quite a lot of real-world applications. Keeping this fact in mind, here, we deal with the fabrication of suitable features to identify human/pedestrian instances from images with near accuracy. Accordingly, we introduce second-order aggregate channel features (SOACF) to enhance the performance of much-celebrated pedestrian detection algorithm which was mainly based on the first-order information in an image—aggregate channel features detector (ACF detector). We experimentally proved the complementary nature of ACF and SOACF. Designed to garner both these features together, instead of simple concatenation, or direct merging of the two detectors, we employed a weighted non-maximum suppression merging algorithm. The prospective detector not only performed well on INRIA, Caltech and KITTI pedestrian data set but also, mitigate the miss rate by $$\sim 4\%$$ ∼ 4 % in Caltech data set and $$\sim 2\%$$ ∼ 2 % in KITTI data set in comparison with ACF detector. Despite the fact that our in-house generated detector uses only a few channels, it surpasses many state-of-the-art methods based on baseline ACF detector. Moreover, the detection speed is 100 times faster than the topmost pedestrian detector based on ACF. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Multimedia Information Retrieval Springer Journals

Pedestrian detection using first- and second-order aggregate channel features

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Publisher
Springer Journals
Copyright
Copyright © 2019 by Springer-Verlag London Ltd., part of Springer Nature
Subject
Computer Science; Multimedia Information Systems; Information Storage and Retrieval; Information Systems Applications (incl.Internet); Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Database Management
ISSN
2192-6611
eISSN
2192-662X
DOI
10.1007/s13735-019-00171-0
Publisher site
See Article on Publisher Site

Abstract

The content-based analysis of visual multimedia like images and videos are urgently needed to empower human society for the automation of difficult tasks. Pedestrian detection serves as a backbone for a multitude of image processing and machine learning algorithms and secures quite a lot of real-world applications. Keeping this fact in mind, here, we deal with the fabrication of suitable features to identify human/pedestrian instances from images with near accuracy. Accordingly, we introduce second-order aggregate channel features (SOACF) to enhance the performance of much-celebrated pedestrian detection algorithm which was mainly based on the first-order information in an image—aggregate channel features detector (ACF detector). We experimentally proved the complementary nature of ACF and SOACF. Designed to garner both these features together, instead of simple concatenation, or direct merging of the two detectors, we employed a weighted non-maximum suppression merging algorithm. The prospective detector not only performed well on INRIA, Caltech and KITTI pedestrian data set but also, mitigate the miss rate by $$\sim 4\%$$ ∼ 4 % in Caltech data set and $$\sim 2\%$$ ∼ 2 % in KITTI data set in comparison with ACF detector. Despite the fact that our in-house generated detector uses only a few channels, it surpasses many state-of-the-art methods based on baseline ACF detector. Moreover, the detection speed is 100 times faster than the topmost pedestrian detector based on ACF.

Journal

International Journal of Multimedia Information RetrievalSpringer Journals

Published: Apr 11, 2019

References