Abstract
Most of the Thermal (Infrared) cameras nowadays are equipped with a motorized lens for focusing a scene manually. The subjective nature of manual focusing makes it an inefficient and cumbersome process. In contrast, Autofocusing (AF) obtains the best focused image based on a quantitative measure with the benefits of convenience and intelligence. Various AF systems for visual cameras have been developed, but relatively less amount of work has been done for thermal imaging systems. This paper presents a Vision and Control based Autofocusing System (VCAFS) comprising: (1) an uncooled thermal camera with motorized lens, (2) a passive contrast-based focus measure, (3) a smoothing operator to avoid local extrema, and (4) two different lens motion controllers. Experimental results show the efficacy of the proposed system on live videos even when the scene and its depth are continuously changing.
Highlights
Image acquisition is an important task in a wide variety of applications such as security, medical science, law enforcement, agriculture, entertainment and power industry, resulting in an increase in popularity of digital cameras
Contrast-based Autofocus System (AFS) does not require the use of AF sensor, secondary mirror or micro-lens because the image captured by the main sensor is used directly
The difference between the two controllers is the step size applied at lines 8 and 16. Results of applying both the proposed controllers on live video feed of a human face are presented
Summary
Image acquisition is an important task in a wide variety of applications such as security, medical science, law enforcement, agriculture, entertainment and power industry, resulting in an increase in popularity of digital cameras. VCAFS is comprised of (1) an uncooled thermal camera with motorized lens, (2) a passive contrast-based focus measure, (3) a smoothing operator to avoid local extrema, and (4) two different lens motion controllers. Contrast-based AFS does not require the use of AF sensor, secondary mirror or micro-lens because the image captured by the main sensor is used directly D. CONTRIBUTIONS OF THE PAPER As evident from literature review, most of the autofocusing work has been based on the comparison and evaluation of focus measures using databases, with little attention to their implementation in real time systems. We compare different focusing methods and find a suitable focusing measure for thermal imagery; we propose a control framework for autofocusing a thermal camera (which does not provide feedback about its lens position) on live video feed with continuously changing scene and depth. A bang-bang controller in two different configurations based on fixed and adaptive step size, along with a moving average filter to avoid local extrema, is devised and implemented to control the motorized lens of an uncooled thermal camera
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