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Proceedings Paper

Deep learning based moving object detection for oblique images without future frames
Author(s): Won Yeong Heo; Seongjo Kim; DeukRyeol Yoon; Jongmin Jeong; HyunSeong Sung
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Paper Abstract

Moving object detection from UAV/aerial images is one of the essential tasks in surveillance systems. However, most of the works did not take account of the characteristics of oblique images. Also, many methods use future frames to detect moving objects in the current frame, which causes delayed detection. In this paper, we propose a deep learning based moving object detection method for oblique images without using future frames. Our network has a CNN (Convolutional Neural Network) architecture with the first and second layer containing sublayers with different kernel sizes. These sublayers play a role in detecting objects with different sizes or speeds, which is very important because objects that are closer to the camera look bigger and faster in oblique images. Our network takes the past five frames registered with respect to the last frame and produces a heatmap prediction for moving objects. Finally, we process a threshold processing to distinguish between object pixels and non-object pixels. We present the experimental results on our dataset. It contains about 15,000 images for training and about 6,000 images for testing with ground truth annotations for moving objects. We demonstrate that our method shows a better performance than some previous works.

Paper Details

Date Published: 24 April 2020
PDF: 6 pages
Proc. SPIE 11394, Automatic Target Recognition XXX, 1139403 (24 April 2020);
Show Author Affiliations
Won Yeong Heo, Agency for Defense Development (Korea, Republic of)
Seongjo Kim, Agency for Defense Development (Korea, Republic of)
DeukRyeol Yoon, Agency for Defense Development (Korea, Republic of)
Jongmin Jeong, Agency for Defense Development (Korea, Republic of)
HyunSeong Sung, Agency for Defense Development (Korea, Republic of)

Published in SPIE Proceedings Vol. 11394:
Automatic Target Recognition XXX
Riad I. Hammoud; Timothy L. Overman; Abhijit Mahalanobis, Editor(s)

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