Dim and Small Targets Detection in Sequence Images Based on Spatiotemporal Motion Characteristics

Fan Xiangsuo, Hongwei Guo, Xu Zhiyong and Biao Li

Mathematical Problems in Engineering, 2020, vol. 2020, 1-19

Abstract:

In order to effectively enhance the low detection rates of dim and small targets caused by dynamic backgrounds, this paper proposes a detection algorithm for dim and small targets in sequence images based on spatiotemporal motion characteristics. With regard to the spatial domain, this paper proposes an improved anisotropic background filtering algorithm that makes full use of the gradient differences between the target and the background pixels in the eight directions of the spatial domain and selects the mean value of the three directions with the lowest diffusion function in the eight directions as the differential filter to obtain a differential image. Then, the paper proposes a directional energy correlation enhancement algorithm in the time domain. Finally, based on the above preprocessing operations, we construct a dim and small targets detection algorithm in sequence images with local motion characteristics, which achieves target detection by determining the number of occurrences of the target, the number of displacements, and the total cumulative area in these sequential images. Experiments show that the proposed detection algorithm in this paper can effectively improve the detection of dim and small targets in dynamic scenes.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:7164859

DOI: 10.1155/2020/7164859

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