Using background model and shallow convolutional neural network to detect moving vehicles from satellite videos

Renxi Chen; Lu Wang; Mengli Zhang; Xinhui Li; Shengyang Li

doi:10.1117/12.2586733

12 March 2021 Using background model and shallow convolutional neural network to detect moving vehicles from satellite videos

Renxi Chen, Lu Wang, Mengli Zhang, Xinhui Li, Shengyang Li

Author Affiliations +

Proceedings Volume 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications; 117633S (2021) https://doi.org/10.1117/12.2586733
Event: Seventh Symposium on Novel Photoelectronic Detection Technology and Application 2020, 2020, Kunming, China

Abstract

In recent years, the Earth-observing satellites have obtained the ability to capture city-scale videos, which enable potential vehicle monitoring. Because of the broad field-of-view, the moving vehicles in satellite videos are very small, making it difficult to differentiate true objects from noise. This paper proposes a terse framework that can effectively suppress false targets while keeping a high detection ratio. The framework first applies the K-nearest neighbor (KNN) background subtraction model to produce preliminary detection results at high recall but with low accuracy, and then uses a shallow convolutional neural network (CNN) to eliminate false targets, increasing the detection accuracy. The experiments and evaluations demonstrate that our method can largely improve the accuracy at the expense of a slight reduction of recall.

Citation Download Citation

Renxi Chen, Lu Wang, Mengli Zhang, Xinhui Li, and Shengyang Li "Using background model and shallow convolutional neural network to detect moving vehicles from satellite videos", Proc. SPIE 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications, 117633S (12 March 2021); https://doi.org/10.1117/12.2586733

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