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• W2885205791 abstract "Tracking of moving objects in video sequences is an important research problem because of its many industrial, biomedical, and security applications. Significant progress has been made on this topic in the last few decades. However, the ability to track objects accurately in video sequences that have challenging conditions and unexpected events, e.g. background motion and shadows; objects with different sizes and contrasts; a sudden change in illumination; partial object camouflage; and low signal‐to‐noise ratio, remains an important research problem. To address such difficulties, the authors developed a robust multiscale visual tracker that represents a captured video frame as different subbands in the wavelet domain. It then applies N independent particle filters to a small subset of these subbands, where the choice of this subset of wavelet subbands changes with each captured frame. Finally, it fuses the outputs of these N independent particle filters to obtain final position tracks of multiple moving objects in the video sequence. To demonstrate the robustness of their multiscale visual tracker, they applied it to four example videos that exhibit different challenges. Compared to a standard full‐resolution particle filter‐based tracker and a single wavelet subband (LL) 2 ‐based tracker, their multiscale tracker demonstrates significantly better tracking performance." @default.
• W2885205791 created "2018-08-22" @default.
• W2885205791 creator A5006489671 @default.
• W2885205791 creator A5089569728 @default.
• W2885205791 date "2018-08-30" @default.
• W2885205791 modified "2023-09-26" @default.
• W2885205791 title "Robust tracking of multiple objects in video by adaptive fusion of subband particle filters" @default.
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• W2885205791 doi "https://doi.org/10.1049/iet-cvi.2018.5376" @default.
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