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This paper establishes a real-time auto-exposure method to guarantee that surveillance cameras in uncontrolled light conditions take advantage of their whole dynamic range while provide neither under nor overexposed images. State-of-the-art auto-exposure methods base their control on the brightness of the image measured in a limited region where the foreground objects are mostly located. Unlike these methods, the proposed algorithm establishes a set of indicators based on the image histogram that defines its shape and position. Furthermore, the location of the objects to be inspected is likely unknown in surveillance applications. Thus, the whole image is monitored in this approach. To control the camera settings, we defined a parameters function (Ef ) that linearly depends on the shutter speed and the electronic gain; and is inversely proportional to the square of the lens aperture diameter. When the current acquired image is not overexposed, our algorithm computes the value of Ef that would move the histogram to the maximum value that does not overexpose the capture. When the current acquired image is overexposed, it computes the value of Ef that would move the histogram to a value that does not underexpose the capture and remains close to the overexposed region. If the image is under and overexposed, the whole dynamic range of the camera is therefore used, and a default value of the Ef that does not overexpose the capture is selected. This decision follows the idea that to get underexposed images is better than to get overexposed ones, because the noise produced in the lower regions of the histogram can be removed in a post-processing step while the saturated pixels of the higher regions cannot be recovered.
The proposed algorithm was tested in a video surveillance camera placed at an outdoor parking lot surrounded by buildings and trees which produce moving shadows in the ground. During the daytime of seven days, the algorithm was running alternatively together with a representative auto-exposure algorithm in the recent literature. Besides the sunrises and the nightfalls, multiple weather conditions occurred which produced light changes in the scene: sunny hours that produced sharpen shadows and highlights; cloud coverages that softened the shadows; and cloudy and rainy hours that dimmed the scene. Several indicators were used to measure the performance of the algorithms. They provided the objective quality as regards: the time that the algorithms recover from an under or over exposure, the brightness stability, and the change related to the optimal exposure. The results demonstrated that our algorithm reacts faster to all the light changes than the selected state-of-the-art algorithm. It is also capable of acquiring well exposed images and maintaining the brightness stable during more time. Summing up the results, we concluded that the proposed algorithm provides a fast and stable auto-exposure method that maintains an optimal exposure for video surveillance applications. Future work will involve the evaluation of this algorithm in robotics.
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