@article {10.3844/ajassp.2015.847.856,
article_type = {journal},
title = {Pedestrian Detection in RGB-D Data Using Deep Autoencoders},
author = {Kazantsev, Pavel Aleksandrovich and Skribtsov, Pavel Vyacheslavovich},
volume = {12},
year = {2015},
month = {Nov},
pages = {847-856},
doi = {10.3844/ajassp.2015.847.856},
url = {https://thescipub.com/abstract/ajassp.2015.847.856},
abstract = {Recent popularity of RGB-D sensors mostly comes from the fact that RGB-images and depth maps supplement each other in machine vision tasks, such as object detection and recognition. This article addresses a problem of RGB and depth data fusion for pedestrian detection. We propose pedestrian detection algorithm that involves fusion of outputs of 2D- and 3D-detectors based on deep autoencoders. Outputs are fused with neural network classifier trained using a dataset which entries are represented by pairs of reconstruction errors of 2D- and 3D-autoencoders. Experimental results show that fusing outputs almost totally eliminate false accepts (precision is 99.8%) and brings recall to 93.2% when tested on the combined dataset that includes a lot of samples with significantly distorted human silhouette. Though we use walking pedestrians as objects of interest, there are few pedestrian-specific processing blocks in this algorithm, so, in general, it can be applied to any type of objects.},
journal = {American Journal of Applied Sciences},
publisher = {Science Publications}
}