Identifying moving objects in a video sequence is a fundamental and critical task in many computer-vision applications. Background subtraction techniques are commonly used to separate foreground moving objects from the background. Most background subtraction techniques assume a single rate of adaptation, which is inadequate for complex scenes such as a traffic intersection where objects are moving at different and varying speeds. In this paper, we propose a foreground validation algorithm that first builds a foreground mask using a slow-adapting Kalman filter, and then validates individual foreground pixels by a simple moving object model built using both the foreground and background statistics as well as the frame difference. Ground-truth experiments with urban traffic sequences show that our proposed algorithm significantly improves upon results using only Kalman filter or frame-differencing, and outperforms other techniques based on mixture of Gaussians, median filter, and approximated median filter.
To cope with this, Cheung and Kamath REF propose a dual-stage algorithm that first builds a foreground mask using a slow-adapting Kalman filter, and then validates individual foreground pixels by a simple moving object model, built using foreground and background statistics as well as the frame difference.
Robust Background Subtraction with Foreground Validation for Urban Traffic Video
Computer Science