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.
@article{arxiv.1402.0289,
title = {A Robust Framework for Moving-Object Detection and Vehicular Traffic Density Estimation},
author = {Pranam Janney and Glenn Geers},
journal= {arXiv preprint arXiv:1402.0289},
year = {2014}
}