Multi-scale particle filtering for multiple object tracking in video sequences

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dc.contributor.authorMahmoud, Ahmed
dc.contributor.examiningcommitteeYahampath, Pradeepa (Electrical and Computer Engineering) Irani, Pourang (Computer Science) Wong, Alexander (Systems Design Engineering, University of Waterloo)en_US
dc.contributor.supervisorSherif, Sherif (Electrical and Computer Engineering)en_US
dc.date.accessioned2018-09-06T21:18:28Z
dc.date.available2018-09-06T21:18:28Z
dc.date.issued2018-08-30en_US
dc.date.submitted2018-09-06T06:23:37Zen
dc.date.submitted2018-09-06T19:48:32Zen
dc.date.submitted2018-09-06T19:58:01Zen
dc.degree.disciplineElectrical and Computer Engineeringen_US
dc.degree.levelDoctor of Philosophy (Ph.D.)en_US
dc.description.abstractThe tracking of moving objects in video sequences, also known as visual tracking, involves the estimation of positions, and possibly velocities, of these objects. Visual tracking is an important research problem because of its many industrial, biomedical, and security applications. Significant progress has been made on this topic over the last few decades. However, the ability to track objects accurately in video sequences having challenging conditions and unexpected events, e.g., background motion, object shadow, 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, we adopted a multi-scale Bayesian approach to develop robust multiple object trackers. We introduce a novel concept in the field of visual tracking by adaptively fusing tracking results obtained from a fixed or variable number of wavelet subbands, corresponding to different scene directions and object scales, of a given video frame. Previous approaches to visual tracking were based on using the full- resolution video frame or a smoothed version of it. These approaches have limitations that were overcome by our multi-scale approach that is described in detail in this thesis. This thesis describes the design and implementation of four novel multi-scale visual trackers that are based on particle filtering and the adaptive fusion of subband frames generated using wavelets. We evaluated the performance of our novel trackers using different video sequences from the CAVIAR and VISOR databases. Compared to a standard full-resolution particle filter-based tracker, and a single wavelet subband (LL)2 based tracker, our multi-scale trackers demonstrate significantly more accurate tracking performance, in addition to a reduction in average frame processing time.en_US
dc.description.noteFebruary 2019en_US
dc.identifier.citationAhmed Mahmoud and Sherif S. Sherif, “Robust Tracking of Multiple Objects in Video by Adaptive Fusion of Subband Particle Filters,” IET computer Vision, July 31th, 2018.en_US
dc.identifier.citationAhmed Mahmoud and Sherif S. Sherif, “Dual-tree complex wavelet transform for robust visual tracking of multiple objects in video,” 11th International Conference on Electrical Engineering (ICEENG), Military Technical College, Cairo, Egypt, 3-5 April, 2018.en_US
dc.identifier.urihttp://hdl.handle.net/1993/33256
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectVisual tracking, Video Trackingen_US
dc.titleMulti-scale particle filtering for multiple object tracking in video sequencesen_US
dc.typedoctoral thesisen_US
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