Vehicles Detection Based on Background Modeling

TL;DR

This paper introduces a vehicle detection system that combines background image subtraction with deep learning validation, using block-based variance modeling and four different methods, with DCT achieving the highest accuracy.

Contribution

It proposes a novel vehicle detection approach integrating background subtraction with deep learning validation and compares four different block-based methods.

Findings

DCT method yields the highest detection accuracy

Block-based variance modeling improves detection robustness

Deep learning validation enhances false positive reduction

Abstract

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.