  Background image subtraction algorithm is a common approach which detects
moving objects in a video sequence by finding the significant difference
between the video frames and the static background model. This paper presents a
developed system which achieves vehicle detection by using background image
subtraction algorithm based on blocks followed by deep learning data validation
algorithm. The main idea is to segment the image into equal size blocks, to
model the static reference background image (SRBI), by calculating the variance
between each block pixels and each counterpart block pixels in the adjacent
frame, the system implemented into four different methods: Absolute Difference,
Image Entropy, Exclusive OR (XOR) and Discrete Cosine Transform (DCT). The
experimental results showed that the DCT method has the highest vehicle
detection accuracy.
