Cascaded Feature Fusion Network for Small-size Pedestrian Detection

Authors: yushilong;yangchenhui

Conference: ICIC 2024 Posters, Tianjin, China, August 5-8, 2024

Pages: 1-12

Keywords: Cascaded convolutional neural network (CNN), Pedestrian Detection, Resid-ual Attention, Image Processing.

DOI: 10.65286/icic.v20i1.10330

Abstract

Deep neural network-based target detectors cannot sufficiently extract effec-tive features for detecting small-size pedestrians. In this letter, we propose a deep cascaded network framework for small-size pedestrian detection, which contains an Iterative Feature Augmentation module and a Residual Attention Fusion module. Specifically, the Iterative Feature Augmentation module adopts bilinear interpolation sampling and channel reshaping in the deep backbone network to achieve feature fusion at different scales. Moreover, we also introduce a feature fusion coefficient to select small-size features. The Residual Attention Fusion module is constructed by stacking attention modules, and the attention modules at different depths produce adaptive changes in perceptual features. Each attention module is a bottom-up feed-forward structure and features are reconstructed by residual connection be-tween attention modules. Experiments on Tiny Citypersons, Caltech, and Ti-ny Person challenging datasets show that our proposed modules achieve sig-nificant gains, with an almost 10% improvement in pedestrian average miss rate and precision compared to baseline networks.

BibTeX Citation:

@inproceedings{ICIC2024,
    author = {yushilong;yangchenhui },
    title = {Cascaded Feature Fusion Network for Small-size Pedestrian Detection },
    booktitle = {Proceedings of the 20th International Conference on Intelligent Computing (ICIC 2024)},
    month = {August},
    date = {5-8},
    year = {2024},
    address = {Tianjin, China},
    pages = {1-12},
    note = {Poster Volume Ⅰ}
    doi = {10.65286/icic.v20i1.10330}
}