Robust Background Subtraction with Foreground Validation for Urban Traffic Video

Journal Article · Thu Jan 15 04:00:00 EST 2004 · Eurasip Journal on Applied Signal Processing

OSTI ID:859918

Cheung, S S; Kamath, C

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 media filter.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA
• Sponsoring Organization: USDOE
• DOE Contract Number: W-7405-ENG-48
• OSTI ID: 859918
• Report Number(s): UCRL-JRNL-201916
• Journal Information: Eurasip Journal on Applied Signal Processing, Journal Name: Eurasip Journal on Applied Signal Processing Vol. 14
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
ALGORITHMS
GROUND TRUTH MEASUREMENTS
MIXTURES
STATISTICS
VALIDATION