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Study Of Moving Target Tracking Based On Incremental Subspace Learning

Posted on:2015-09-15Degree:MasterType:Thesis
Country:ChinaCandidate:L ZhangFull Text:PDF
GTID:2348330482456328Subject:Computational Mathematics
Abstract/Summary:PDF Full Text Request
Visual tracking is an important branch of computer vision research area. So far, we have made great progress on visual tracking research and various kinds of visual tracking methods have been proposed. These methods differ in several aspects such as target object's representation, Image feature selection, modeling of target object's, motion, appearance and shape. In Real applications, the performance of visual tracking will be commonly affected by the appearance variation of target objects. And related system also achieved good effect in practical application. Most existing tracking algorithms construct a representation of a target object prior to the tracking task starts, and utilize in variant features to handle appearance variation of the target caused by lighting, Pose, and view angle change. It has been proved that in variant feature will not reflect the appearance variation caused by some intrinsic and extrinsic factors.In order to adapt the appearance variation of target objects during the tracking process, the target object is represented by its appearance feature subspace, which will be updated constantly and efficiently during the tracking process using an incremental principal component analysis method.Moreover he posterior probability distribution is propagated by generating a series of samples according to the tracking result of the previous frame, using a kind of particle filter method which derives from Condensation method. Then the tracking result is calculated with an efficient similarity measurement. Experiments demonstrate the effectiveness of our method in the environments where the target object undergoes appearance variation due to object translation, rotation and illumination changes.In this paper, we present a tracking method that incrementally learns a low-dimensional subspace representation, efficiently adapting online to changes in the appearance of the target which derives from Condensation method. And this method is not limited to a specific device requirements, testing effect is good, easy to implement.
Keywords/Search Tags:object tracking, online algorithms, condensation, appearance model, motion model, principal component analysis
PDF Full Text Request
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