Image brightness correction for frame-difference-based intelligent video monitoring systems

In frame-difference-based intelligent video monitoring systems, changes of image brightness can seriously influence the results of target object recognizing and tracking. After comparing some common brightness correction methods, the direct proportion transformation method was selected for further study. To meet the effect requirement, the transformation factor was calculated according to the average grayscale of a manually preselected region for avoiding influence of target object. To meet the real time requirement, the correction process was started only when the brightness difference between two continuous frames was greater than a certain threshold. When programming with Visual C++ 6.0, some further measures were adopted to speed up the process, such as transforming floating-point calculation into integer calculation. Experiment results show that, target objects that cannot be recognised before correction can be recognised after correction, the time consumption is about 1.2 ms for correction of one frame, and the comprehensive effect and speed reach the best when the threshold is 1 or 2.