NDLI: Real-time multi-scale tracking based on compressive sensing

Content Provider	Springer Nature Link
Author	Wu, Yunxia Jia, Ni Sun, Jiping
Copyright Year	2014
Abstract	Tracking-by-detection methods have been widely studied and some promising results have been obtained. These methods use discriminative appearance models to train and update online classifiers. They also use a sliding window to detect samples which will then be classified. Then, the location of the sample with the maximum classifier response will be selected as the new location. Compressive tracking was recently proposed with an appearance model based on features extracted in the compressed domain. However, CT uses a fixed-size tracking box to detect samples, and this is unsuitable for practice applications. CT detects samples around the selected region of the previous frame within a fixed radius. Here, the classifier may become inaccurate if the selected region drifts. The fixed radius is also not suitable for tracking targets that experience abrupt acceleration changes. Furthermore, CT updates the classifier parameters with constant learning rate. If the target is fully occluded for an extended period, the classifier will instead learn the features of the cover object and the target will ultimately be lost. In this paper, we present a multi-scale compressive tracker. This tracker integrates an improved appearance model based on normalized rectangle features extracted in the adaptive compressive domain into the bootstrap filter. This type of feature extraction is efficient, and the computation complexity does not increase as the tracking regions become larger. The classifier response is utilized to generate particle importance weight and a re-sample procedure preserves samples according to weight. A 2-order transition model considers the target velocity to estimate the current position and scale status. In this way, the sampling is not limited to a fixed range. Here, feedback strategies are adopted to adjust learning rate for occlusion. Experimental results on various benchmark challenging sequences have demonstrated the superior performance of our tracker when compared with several state-of-the-art tracking algorithms.
Starting Page	471
Ending Page	484
Page Count	14
File Format	PDF
ISSN	01782789
Journal	The Visual Computer
Volume Number	31
Issue Number	4
e-ISSN	14322315
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-04-30
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Tracking-by-detection Multi-scale Compressive tracking Bootstrap filter Real time Computer Graphics Computer Science Artificial Intelligence (incl. Robotics) Image Processing and Computer Vision
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Computer Vision and Pattern Recognition Software

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