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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Weiguo Feng Rui Liu Baozhi Jia Ming Zhu |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. of Electron. Eng. & Inf. Sci., Univ. of Sci. & Technol. of China, Hefei, China (Weiguo Feng; Rui Liu) || Dept. of Autom., Univ. of Sci. & Technol. of China, Hefei, China (Baozhi Jia; Ming Zhu) |
| Abstract | Moving object detection is often one of the most basic and important stages in computer vision applications. In this paper, a novel background model is proposed to extract moving foreground objects from videos that may contain different kinds of disturbance such as illumination changes, camera parameter variations, noises and dynamic backgrounds, etc. For each frame, a local frequency response map is generated using short-term Fourier transformation (STFT) in local regions, and by extracting the relations among neighborhoods of the response map, a compact pixel feature is introduced as local frequency pattern. Then, an adaptive probabilistic estimation of pixel feature sequence modified from kernel density estimation is performed to estimate the probability of a pixel being background. Experimental evaluations on complex scenes of surveillance videos demonstrate that the proposed method has archived satisfactory results. |
| Starting Page | 389 |
| Ending Page | 394 |
| File Size | 449644 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479907038 |
| DOI | 10.1109/AVSS.2013.6636671 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-08-27 |
| Publisher Place | Poland |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Adaptation models Surveillance Estimation Object detection Feature extraction Robustness Kernel |
| Content Type | Text |
| Resource Type | Article |
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