An Improved Fast Compressive Tracking Algorithm Based on Online Random Forest Classifier

Jintao Xiong, Pan Jiang, Jianyu Yang, Zhibin Zhong, Ran Zou, Baozhong Zhu, H.F. Abdul Amir, A.M. Korsunsky, Z. Guo
2016 MATEC Web of Conferences  
The fast compressive tracking (FCT) algorithm is a simple and efficient algorithm, which is proposed in recent years. But, it is difficult to deal with the factors such as occlusion, appearance changes, pose variation, etc in processing. The reasons are that, Firstly, even if the naive Bayes classifier is fast in training, it is not robust concerning the noise. Secondly, the parameters are required to vary with the unique environment for accurate tracking. In this paper, we propose an improved
more » ... ast compressive tracking algorithm based on online random forest (FCT-ORF) for robust visual tracking. Firstly, we combine ideas with the adaptive compressive sensing theory regarding the weighted random projection to exploit both local and discriminative information of the object. The second reason is the online random forest classifier for online tracking which is demonstrated with more robust to the noise adaptively and high computational efficiency. The experimental results show that the algorithm we have proposed has a better performance in the field of occlusion, appearance changes, and pose variation than the fast compressive tracking algorithm's contribution. Index Terms-fast compressive tracking, naive Byes classifier, online, random forest. R r is a random projection matrix, and n m . According to Johnson-Lindenstrauss [2] lemma, for the two K-sparse vectors, the following property can be obtained to ensure the integrity of important information. , Web of Conferences
doi:10.1051/matecconf/20165901003 fatcat:py26xmma6rdmbduiifnz3omcwu