Visual-Attention-Based Background Modeling for Detecting Infrequently Moving Objects

Lin, Yuewei; Tong, Yan; Cao, Yu; Zhou, Youjie; Wang, Song

doi:10.1109/TCSVT.2016.2527258

Title: Visual-Attention-Based Background Modeling for Detecting Infrequently Moving Objects

Journal Article · Mon Feb 08 00:00:00 EST 2016 · IEEE Transactions on Circuits and Systems for Video Technology

DOI:https://doi.org/10.1109/TCSVT.2016.2527258· OSTI ID:1491679

^[1]; Tong, Yan ^[1]; Cao, Yu ^[2]; Zhou, Youjie ^[1]; Wang, Song ^[1]

Univ. of South Carolina, Columbia, SC (United States). Dept. of Computer Science and Engineering
IBM Almaden Research Center, San Jose, CA (United States); Univ. of South Carolina, Columbia, SC (United States)

Motion is one of the most important cues to separate foreground objects from the background in a video. Using a stationary camera, it is usually assumed that the background is static, while the foreground objects are moving most of the time. However, in practice, the foreground objects may show infrequent motions, such as abandoned objects and sleeping persons. Meanwhile, the background may contain frequent local motions, such as waving trees and/or grass. Such complexities may prevent the existing background subtraction algorithms from correctly identifying the foreground objects. We propose a new approach that can detect the foreground objects with frequent and/or infrequent motions. Specifically, we use a visual-attention mechanism to infer a complete background from a subset of frames and then propagate it to the other frames for accurate background subtraction. Furthermore, we develop a feature-matching-based local motion stabilization algorithm to identify frequent local motions in the background for reducing false positives in the detected foreground. The proposed approach is fully unsupervised, without using any supervised learning for object detection and tracking. Extensive experiments on a large number of videos have demonstrated that the proposed approach outperforms the state-of-the-art motion detection and background subtraction methods in comparison.

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Research Organization:: Univ. of South Carolina, Columbia, SC (United States)

Sponsoring Organization:: National Science Foundation (NSF); Army Research Laboratory (ARL)

Grant/Contract Number:: IIS-1017199; IIS-1149787; W911NF-10-2-0060

OSTI ID:: 1491679

Report Number(s):: BNL-210902-2019-JAAM

Journal Information:: IEEE Transactions on Circuits and Systems for Video Technology, Vol. 27, Issue 6; ISSN 1051-8215

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 30 works

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