Real-time object tracking via compressive feature selection

• Published in: Frontiers of Computer Science 2016-08, Vol.10 (4), p.689-701
• Main Authors: LI, Kang, HE, Fazhi, CHEN, Xiao
• Format: Article
• Language: English
• Subjects: Algorithms; Classifiers; compressive sensing; Computer Science; Feature extraction; Feature selection; object tracking; real-time; Research Article; supervised learning; Tracking; 在线压缩; 外观变化; 实时; 特征选择; 目标跟踪; 贝叶斯分类器; 选择算法; 随机预测
• ISSN: 2095-2228; 2095-2236
• DOI: 10.1007/s11704-016-5106-5

Recently, compressive tracking (CT) has been widely proposed for its efficiency, accuracy and robustness on many challenging sequences. Its appearance model employs non-adaptive random projections that preserve the structure of the image feature space. A very sparse measurement matrix is used to extract features by multiplying it with the feature vector of the image patch. An adaptive Bayes classifier is trained using both positive samples and negative samples to separate the target from background. On the CT framework, however, some features used for classification have weak discriminative abilities, which reduces the accuracy of the strong classifier. In this paper, we present an online compressive feature selection algorithm(CFS) based on the CT framework. It selects the features which have the largest margin when using them to classify positive samples and negative samples. For features that are not selected, we define a random learning rate to update them slowly. It makes those weak classifiers preserve more target information, which relieves the drift when the appearance of the target changes heavily. Therefore, the classifier trained with those discriminative features couples its score in many challenging sequences, which leads to a more robust tracker. Numerous experiments show that our tracker could achieve superior result beyond many state-of-the-art trackers.