Deep Vehicle Detection in Satellite Video
Abstract
This work presents a deep learning approach for vehicle detection in satellite video. Vehicle detection is perhaps impossible in single EO satellite images due to the tininess of vehicles (4-10 pixel) and their similarity to the background. Instead, we consider satellite video which overcomes the lack of spatial information by temporal consistency of vehicle movement. A new spatiotemporal model of a compact convolutional, neural network is proposed which neglects pooling layers and uses leaky ReLUs. Then we use a reformulation of the output heatmap including Non-Maximum-Suppression (NMS) for the final segmentation. Empirical results on two new annotated satellite videos reconfirm the applicability of this approach for vehicle detection. They more importantly indicate that pre-training on WAMI data and then fine-tuning on few annotated video frames for a new video is sufficient. In our experiment only five annotated images yield a score of 0.81 on a new video showing more complex traffic patterns than the Las Vegas video. Our best result on Las Vegas is a score of 0.87 which makes the proposed approach a leading method for this benchmark.
Cite
@article{arxiv.2204.06828,
title = {Deep Vehicle Detection in Satellite Video},
author = {Roman Pflugfelder and Axel Weissenfeld and Julian Wagner},
journal= {arXiv preprint arXiv:2204.06828},
year = {2022}
}