  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.
