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Proceedings Paper

A pedestrian detection algorithm based on deep deconvolution networks in complex scenes
Author(s): Zhi Liu; Yanru Sun; Mengmeng Zhang
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Paper Abstract

Pedestrian detection is an important application in computer vision. Due to uneven illumination, serious obstacles, low quality images, abnormal posture and other factors, pedestrian detection faces the problem of low detection accuracy in complex scenes. In this paper, pedestrian detection algorithm based on deep convolution neural network is studied. Since shorter connections between the input and output layers can help to build deeper and more efficient network in CNN, a densely connected convolution structure is introduced in this paper to optimize the Deconvolutional Single Shot Detector and improve the feature utilization and reduce the network parameters. Meanwhile, by augmenting the context information, the detection performance for small size pedestrians is improved. The initial experimental results show that the proposed algorithm improves the detection accuracy to 87.84% at the speed of 12.3fps on low-resolution (64x128) pedestrian dataset, which outperforms the reference algorithms.

Paper Details

Date Published: 9 August 2018
PDF: 10 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060U (9 August 2018); doi: 10.1117/12.2502815
Show Author Affiliations
Zhi Liu, North China Univ. of Technology (China)
Yanru Sun, North China Univ. of Technology (China)
Mengmeng Zhang, North China Univ. of Technology (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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