Robust Visual Object Tracking mean shift, particle filters and point features

Sammanfattning: Visual object tracking has been identified as a promising technique for many computer vision applications like surveillance, flight navigation, video compression and driver assistance. The main idea is to find the state ofthe object and how it changes over time, in recursive video frames. The state can be arbitrarily complex but usually consists of object shape and appearance. Robust visual tracker has to deal many challenges like object affine transformation, pose change, deformation, partial or full occlusion, complex background or clutter, dynamic camera, image noise, illumination change and real time constraint.This thesis addresses the problems in visual object tracking by combining object appearance, shape and motion of bounding box. The proposed method uses anisotropic mean-shift tracker for appearance similarity while SIR particle filter for tracking of bounding box. It adds spatial informationin histogram calculation by dividing the bounding box into disjoint areas and finds the height, width and orientation of the region by checking the goodness of the sub region bandwidth estimate through Bhattacharyya similarity coefficient. Unlike previous algorithms to embed the mean-shift tracker in PF framework, the proposed method extends the idea in [43] by not only renewing the box center, but also adding the width, height and orientation estimation of region by using multi-mode anisotropic mean shift. The combined scheme is able to maintain the merits of both methods, uses a small number of particles (

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