We present a deep-learning network that detects multiple small objects (hundreds to thousands) in a scene while simultaneously estimating their x,y pixel locations together with a characteristic feature-set (for instance, target orientation and color). All estimations are performed in a single, forward pass which makes implementing the network fast and efficient. In this paper, we describe the architecture of our network --- nicknamed ALIEN --- and detail its performance when applied to vehicle detection.
@article{arxiv.1902.05387,
title = {Simultaneous x, y Pixel Estimation and Feature Extraction for Multiple Small Objects in a Scene: A Description of the ALIEN Network},
author = {Seth Zuckerman and Timothy Klein and Alexander Boxer and Christopher Goldman and Brian Lang},
journal= {arXiv preprint arXiv:1902.05387},
year = {2019}
}