Vehicles Detection Based on Background Modeling
This is an incremental improvement for traffic monitoring systems, focusing on a specific domain.
The paper tackled vehicle detection in video by combining block-based background subtraction with deep learning validation, finding that the Discrete Cosine Transform method achieved the highest accuracy among four tested approaches.
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.