Dr. Ning Chu Profile

Dr. Ning Chu

at Zhejiang Univ

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Proceedings Article | 12 March 2021 Paper

A multi-objective anomaly abnormal detection method based on the infrared and optical image fusion

Ning Chu, Lianrui Mu, Zhiyan Li, Jiarui Ling, Yao Zhong

Proceedings Volume 11763, 11763A3 (2021) https://doi.org/10.1117/12.2588372

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Due to the impact of COVID-19, people are required to wear masks and take the temperature in most public places. A multi-objective detection method for abnormal people is proposed to solve the problem of screening for abnormal people in high crowd density effectively. Based on the infrared and visible light fusion image, the method screens abnormal people with high body temperature or without wearing masks. Firstly, the Maximum A Posterior（MAP） algorithm is used to compensate for the temperature of the acquired infrared grayscale image. Then the compensated infrared image is scaled, translated, colored, and fused with the visible light image through the screen to show the change and situation of body temperature directly. After that, machine learning plays a significant role in recognizing faces and determining whether the target is wearing a mask. Experiments show that this method has a high success rate for mask recognition at different angles and distances, and can effectively screen abnormal persons with high body temperature.

Proceedings Article | 10 October 2020 Presentation + Paper

High-precision thermography based on JMAP inference for human face temperature

Ning Chu, Yao Zhong, Yaochun Hou

Proceedings Volume 11559, 115590I (2020) https://doi.org/10.1117/12.2574940

KEYWORDS: Thermography, Thermal modeling, Infrared cameras, Temperature metrology, Calibration, Precision measurement, Bayesian inference, Physics, Black bodies, Body temperature

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During the pandemic Covid-19, infrared thermography is an efficient way to detect susceptible persons with abnormal temperatures. However, two significant factors seriously limit the temperature precision: measurement uncertainty of the infrared camera and model inaccuracy of thermal radiation. In this paper, we propose the joint maximum a posterior (JMAP) approach with a new hierarchical prior model. The advantages of JMAP are that the Bayesian inference can combine prior model and likelihood model to regulate the uncertainty from both physics and measurements. At first, we obtain the estimated parameters of the thermal radiation model from training-data. We propose that the distribution of actual temperature and the distribution of measurement error satisfy the Gaussian distribution. We take the variance of the Gaussian distribution as the latent variable and assume that the variance satisfies the inverse gamma distribution, which we control by setting hyperparameters, which determine the uncertainty of the temperature variance so that variances are updated continuously rather than constants. We apply the JMAP inference to test human-face temperature for infrared camera calibration. Although the pre-set parameters of the cost-effective infrared camera badly affect the accuracy, our proposed approach can refine the error of less than 0.1 ℃ compared with 1.0℃ without calibration at certain distances. Therefore, our proposed approach can offer an efficient and accurate way to screen people with abnormal body temperature against Covid-19.

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