Object Tracking Using Hybrid Mean Shift & Particle Filter Algorithms
An indepth discussion on computer vision object tracking algorithms
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Object tracking is an ongoing research area in computer vision. Applications of visual tracking algorithms are countless and growing. Object tracking is required and has been attempted in many domains, including surveillance, sports analysis, gesture recognition, medical applications such as microscopic sample analysis, to study traffic and pedestrian flow dynamics for efficient designs of roads and pathways, growth patterns in plants and animal cells, tracking and targeting applications and studying group behaviour in moving animals and humans This book discusses visual tracking algorithms an...
Object tracking is an ongoing research area in computer vision. Applications of visual tracking algorithms are countless and growing. Object tracking is required and has been attempted in many domains, including surveillance, sports analysis, gesture recognition, medical applications such as microscopic sample analysis, to study traffic and pedestrian flow dynamics for efficient designs of roads and pathways, growth patterns in plants and animal cells, tracking and targeting applications and studying group behaviour in moving animals and humans This book discusses visual tracking algorithms and their application in the real world. One of the main challenges of tracking using particle filter-based algorithms is to manage the particle spread. Too wide a spread leads to dispersal of particles onto clutter, but limited spread may lead to difficulty when fast-moving objects and/or high-speed camera motion throw trackers away from their target(s). This book addresses the particle spread management problem. Extensive literature review along with three novel hybrid tracking algorithms are presented.
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