Object Tracking Based on Compressive Features and Extreme Learning Machine
Object Tracking Based on Compressive Features and Extreme Learning Machine
复制标题
基于压缩特征和极限学习机的物体跟踪
DOI：
10.1109/access.2019.2909667
复制
发表时间：
2019-04
期刊：
影响因子：
3.9
通讯作者：
Zheng Liying
中科院分区：
文献类型：
--
作者：
Liu Enhan;Chu Yan;Zheng Liying
Effective object tracking is a great challenging task in computer vision due to the appearance changes of an object. Extreme learning machine (ELM) can analytically determine the weights of feed-forward neural networks, while compressive features can be extracted via a random sparse matrix. Thus, their common advantages are running fast. Moreover, the strong learning ability of ELM is helpful to make a tracker robust target appearance change. In this paper, we propose an effective tracking method using ELM and compressive features. First, a bank of compressive Haar-like features is extracted with the sparse random matrix. To make the feature invariant to illumination, the mean illumination of each image patch is removed from each Haar-like feature. Second, a bank of ELM-based weak classifiers are combined together to form a strong classifier to complete tracking task. Considering the discrimination performance of features is uneven, the training accuracy of each ELM is employed as the confidence (weight) of the weak classifier. Thus, less discriminative features will be weakened. The proposed tracker has been tested on several representative sequences, and the results show that it achieves the highest tracking success rate and the tracking accuracy among the compared trackers including the FCT, CNT, MIL, KCF, and ESPT.
登录
查看更多内容
影响因子：
6
作者：
M. Berger;A. D. Souza;Jorcy de Oliveira Neto;Edilson de Aguiar;Thiago Oliveira-Santos
通讯作者：
M. Berger;A. D. Souza;Jorcy de Oliveira Neto;Edilson de Aguiar;Thiago Oliveira-Santos
DOI：
10.1016/j.patcog.2016.02.012
发表时间：
2016-11
期刊：
Pattern Recognit.
影响因子：
--
作者：
Yuefang Gao;Xin Shan;Zexi Hu;Dong Wang;Ya Li;Xuhong Tian
通讯作者：
Yuefang Gao;Xin Shan;Zexi Hu;Dong Wang;Ya Li;Xuhong Tian
影响因子：
1.1
作者：
Baoxian Wang;Shuigen Wang;Xun Liu;Jinglin Yang
通讯作者：
Baoxian Wang;Shuigen Wang;Xun Liu;Jinglin Yang
影响因子：
6
作者：
Huang, Guang-Bin;Zhu, Qin-Yu;Siew, Chee-Kheong
通讯作者：
Siew, Chee-Kheong
自适应视觉跟踪的在线外观模型学习和生成
DOI：
10.1109/tcsvt.2011.2105598
发表时间：
2011-02
影响因子：
8.4
作者：
Wang, Peng;Qiao, Hong
通讯作者：
Qiao, Hong