A Robust Framework for Moving-Object Detection and Vehicular Traffic Density Estimation

TL;DR

This paper introduces a computationally efficient, noise-robust texture-based method for moving-object detection in videos, and applies it to vehicular traffic density estimation, outperforming existing approaches.

Contribution

It presents a novel, minimal-parameter, noise-resistant texture measure for moving-object detection and integrates it into a traffic density estimation framework, comparing it with classical models.

Findings

Higher detection accuracy than state-of-the-art methods

Effective in noisy, changing illumination, and low frame rate conditions

Outperforms classical density estimation frameworks

Abstract

Intelligent machines require basic information such as moving-object detection from videos in order to deduce higher-level semantic information. In this paper, we propose a methodology that uses a texture measure to detect moving objects in video. The methodology is computationally inexpensive, requires minimal parameter fine-tuning and also is resilient to noise, illumination changes, dynamic background and low frame rate. Experimental results show that performance of the proposed approach is higher than those of state-of-the-art approaches. We also present a framework for vehicular traffic density estimation using the foreground object detection technique and present a comparison between the foreground object detection-based framework and the classical density state modelling-based framework for vehicular traffic density estimation.