Improved Infrared Anomaly Target Detection Algorithm Based on Single Gaussian Model

SONG Shanshan; ZHAI Xuping

Volume 43 Issue 9

Sep. 2021

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SONG Shanshan, ZHAI Xuping. Improved Infrared Anomaly Target Detection Algorithm Based on Single Gaussian Model[J]. Infrared Technology , 2021, 43(9): 885-888,894.

Citation:

SONG Shanshan, ZHAI Xuping. Improved Infrared Anomaly Target Detection Algorithm Based on Single Gaussian Model[J]. Infrared Technology , 2021, 43(9): 885-888,894.

SONG Shanshan, ZHAI Xuping. Improved Infrared Anomaly Target Detection Algorithm Based on Single Gaussian Model[J]. Infrared Technology , 2021, 43(9): 885-888,894.

Citation:

SONG Shanshan, ZHAI Xuping. Improved Infrared Anomaly Target Detection Algorithm Based on Single Gaussian Model[J]. Infrared Technology , 2021, 43(9): 885-888,894.

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Improved Infrared Anomaly Target Detection Algorithm Based on Single Gaussian Model

SONG Shanshan,
ZHAI Xuping^,

Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China

Received Date: 2020-04-23
Rev Recd Date: 2021-01-12
Publish Date: 2021-09-20

Abstract

Abstract

An infrared anomaly target detection algorithm based on a single Gaussian model is a commonly used detection algorithm that can adaptively update the background model. The algorithm performs Gaussian modeling on the output response of each pixel and determines whether the target pixel is a foreground pixel through a defined threshold to realize detection. This paper proposes an improved anomaly detection algorithm based on a single Gaussian model. The algorithm uses the Neiman-Pearson criterion to define the optimal threshold, which overcomes the limitation of selecting the threshold based on empirical values. The paper lays a theoretical foundation for obtaining the best decision threshold so that under a certain false rate, the detection probability can reach the highest value. Experimental results show that, compared to the commonly experienced thresholds, the threshold determined in this study provides a much better detection effect.
- infrared imaging,
- target detection,
- single Gaussian model,
- Neiman-Pearson criterion

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References(17)

References

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