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| Compressive Tracking Algorithm Based on Particle Filter and Sample Weighting |
| ZHANG Hongying WANG Sainan HU Wenbo |
| (College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China) |
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Abstract To solve the problem that Compressive Tracking (CT) algorithm is unable to adapt to the scale change of the object and ignores the sample weight, an optimized compressive tracking algorithm based on particle filter and sample weighting is presented. Firstly, the compressive feature is improved for building a target apparent model with normalized rectangle features. Then, the thought of sample weighting is utilized. In order to increase the precision of the classifier, different weights are given to the positive samples in accordance with the different distances between the positive samples and the object. Finally, the dynamic state estimation is made under the particle filter frame with integrating the scale invariant feature. At the phase of particle prediction, a second-order autoregressive model is utilized to obtain the estimation and prediction of the particle state. The particle state is updated with the observation model. The particles resampling is used to prevent the degradation of particles. Experimental results demonstrate that the improved algorithm can adapt to the scale change of object, and the accuracy and stability of the compressive tracking algorithm is improved.
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Received: 07 September 2017
Published: 23 March 2018
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| Fund:The Natural Science Foundation of Tianjin (12JCQNJC00600), The Fundamental Research Funds for the Central Universities (3122015C016), The National Natural Science Foundation of China (U1533203) |
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Corresponding Authors:
ZHANG Hongying
E-mail: carole_zhang0716@163.com
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