NDLI: Robust tracking of multiple objects in video by adaptive fusion of subband particle filters

Content Provider	IET Digital Library
Author	Mahmoud, Ahmed Sherif, Sherif S.
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.
Starting Page	1207
Ending Page	1218
Page Count	12
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 8, Dec (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5376
Journal	IET Computer Vision
Publisher Date	2018-07-30
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Subband Particle Filter Fusion Background Motion Captured Video Frame Combinatorial Mathematics Computer Vision And Image Processing Technique Filtering Method in Signal Processing Image Fusion Image Sequence Independent Particle Filter Low Signal-to-noise Ratio Moving Object Tracking Object Tracking Optical, Image And Video Signal Processing Partial Object Camouflage Particle Filter Robust Multiple Object Tracking Robust Multiscale Visual Tracker Sensor Fusion Set Theory Shadows Video Sequence Video Signal Processing Wavelet Subband Subset
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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