Improved Model Updating and Feature Selection in Compressive Tracking

• Main Authors: Pei-Chun Tu, 涂佩君
• Other Authors: 顏淑惠
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/76576659374783533112

碩士 === 淡江大學 === 資訊工程學系碩士班 === 104 === The past ten years, compressive sensing is an important discover on the topic of signal processing, it can use a few signals to represent the source signals for the purpose of the real-time computation. In 2012, Zhang et al. in “Real-Time Compressive Tracking” [2] successfully applied it on visual tracking and attracted attention of researchers in computer vision field due to the simple algorithm and low computation. They use a sparse random matrix that satisfies the RIP condition of compressive sensing to reduce the dimension of the multi- scales Haar-like feature of images and build positive and negative Gaussian models for each selected feature. Then, target is localized by the naïve Bayes classifier. However, models are apt to update wrong information when the drift problem happens and the system remains updating models in the tracking. Thus, we propose an improvement to ensure the accuracy of models, which adaptively updates models depending on the score from the classifier. After a large number of experiments, we demonstrate that our improvement increases the accuracy of models by reducing the drift problem. In addition, we find out that the tracking results may be quite different due to randomness in selected features and best feature combination may not be applicable for different tracking scenarios.