A Robust Framework for Moving-Object Detection and Vehicular Traffic Density Estimation
This work addresses the problem of robust moving-object detection and traffic density estimation for intelligent machines and traffic management systems, but it appears incremental as it builds on existing foreground detection techniques.
The paper tackles moving-object detection in video using a texture-based method that is computationally inexpensive and resilient to various challenges like noise and illumination changes, achieving higher performance than state-of-the-art approaches. It also presents a framework for vehicular traffic density estimation, comparing it with classical methods.
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.