Detecting and counting vehicles using adaptive background subtraction and morphological operators in real time systems
Video surveillance is a process of evaluating the sequence of videos and is an important topic in the machine vision. The usual approach is to implement a background subtraction that takes moving objects from a part of the video frame that is significantly different from the background. Many challenges exist in the development of a background subtraction algorithm. First, it must be strong in relation to the variations in brightness intensity. Another is the identification of moving components in the background, such as shaking leaves, rain, Snow and shadows created by passing cars. Finally, the internal background model versus changes in Backgrounds such as turning off and shutting down vehicles quickly react. Therefore, we have explored this method and presented a way to update the background in this article to make this background update more effective than lighting or climate changes. In this process, morphological operators have also been used to eliminate image noises. Show that our proposed method achieves appropriate performance for both detection and counting in dynamic background.
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