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.

Improved Infrared Anomaly Target Detection Algorithm Based on Single Gaussian Model

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  • Received Date: April 22, 2020
  • Revised Date: January 11, 2021
  • 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.
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